diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9c3253ba5..803715e48 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -44,12 +44,22 @@ caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only i
 caffe_option(USE_SYSTEMTAP "Build for SystemTap" OFF)
 caffe_option(PERFORMANCE_MONITORING "Build Caffe with PERFORMANCE_MONITORING " OFF)
 #caffe_option(USE_GITHUB_MKLDNN "Download and use MKL-DNN available on github" OFF)
- 
+caffe_option(CODE_COVERAGE "Build with code coverage instrumentation" OFF)
+caffe_option(CO_SIM "Build readonly cpu blob data/diff pycaffe interface" OFF)
+caffe_option(USE_MLSL "Build Caffe with Intel(r) Machine Learning Scaling Library for multinode training" ON)
+
 # ---[ Dependencies
 include(cmake/MKLDNN.cmake)
 include(cmake/DLCP.cmake)
 include(cmake/Dependencies.cmake)
 
+if(CODE_COVERAGE)
+  # code coverage necessary
+  include(CodeCoverage)
+  APPEND_CCOV_COMPILER_FLAGS()
+endif()
+#end code coverage
+
 # ---[ Flags
 if(UNIX OR APPLE)
 
@@ -81,14 +91,16 @@ if(UNIX OR APPLE)
     # Same as above, with exception that ICC compilation crashes with -fPIE option, even 
     # though it uses -pie linker option that require -fPIE during compilation. Checksec
     # shows that it generates correct PIE anyway if only -pie is provided.
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fstack-protector")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fstack-protector -wd2196")
     # Auto enable SGD Fusion if use intel compiler
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DENABLE_SGD_FUSION")
   endif()
   
   # Generic flags.
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -fno-operator-names -Wformat -Wformat-security -Wall")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+  if(NOT CODE_COVERAGE)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Werror")
+  endif()
   # Dot not forward c++11 flag to GPU beucause it is not supported
   set( CUDA_PROPAGATE_HOST_FLAGS OFF )
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -D_FORTIFY_SOURCE=2")
diff --git a/Makefile b/Makefile
index 5e9d16ad5..d67f27d83 100644
--- a/Makefile
+++ b/Makefile
@@ -387,14 +387,14 @@ endif
 
 # Compiler flags
 ifneq (,$(findstring icpc,$(CXX)))
-	CXX_HARDENING_FLAGS += -fstack-protector
+	CXX_HARDENING_FLAGS += -fstack-protector -wd2196
 	#Enable SGD FUSION if use intel compiler
 	COMMON_FLAGS += -DENABLE_SGD_FUSION
 
 else ifneq (,$(findstring clang++,$(CXX)))
 	CXX_HARDENING_FLAGS += -fPIE -fstack-protector
 else ifneq (,$(findstring g++,$(CXX)))
-	ifeq ($(shell echo | awk '{ print $(GCCVERSION) >= 4.9 }'), 1)
+	ifeq ($(shell echo | awk '{print $(GCCVERSION) < 4.9;}'), 0)
 		CXX_HARDENING_FLAGS += -fPIE -fstack-protector-strong
 		#Enable SGD FUSION if gcc version >= 4.9
 		COMMON_FLAGS += -DENABLE_SGD_FUSION
@@ -415,7 +415,7 @@ else
 endif
 
 # Generic flags
-CXX_HARDENING_FLAGS += -fPIC -fno-operator-names -Wformat -Wformat-security -Wall
+CXX_HARDENING_FLAGS += -fPIC -fno-operator-names -Wformat -Wformat-security -Wall -Werror
 LINKER_EXEC_HARDENING_FLAGS += -pie
 
 # Release-only flag
@@ -493,11 +493,26 @@ ifeq ($(DISABLE_BN_FOLDING), 1)
 	COMMON_FLAGS += -DDISABLE_BN_FOLDING
 endif
 
+# Disable Conv + ReLU fusion
+ifeq ($(DISABLE_CONV_RELU_FUSION), 1)
+    COMMON_FLAGS += -DDISABLE_CONV_RELU_FUSION
+endif
+
+# Disable Bn + ReLU fusion
+ifeq ($(DISABLE_BN_RELU_FUSION), 1)
+    COMMON_FLAGS += -DDISABLE_BN_RELU_FUSION
+endif
+
 # Disable the conv/eltwise/relu layer fusion
 ifeq ($(DISABLE_CONV_SUM_FUSION), 1)
 	COMMON_FLAGS += -DDISABLE_CONV_SUM_FUSION
 endif
 
+# Disable sparse
+ifeq ($(DISABLE_SPARSE), 1)
+	COMMON_FLAGS += -DDISABLE_SPARSE
+endif
+
 # Performance monitoring
 ifeq ($(PERFORMANCE_MONITORING), 1)
 	CXXFLAGS += -DPERFORMANCE_MONITORING
@@ -515,7 +530,9 @@ include Makefile.dlcp
 BOOST_LDFLAGS += $(foreach boost_lib,$(BOOST_LIBRARIES),-l$(boost_lib))
 ifneq ($(origin BOOST_ROOT), undefined)
 	INCLUDE_DIRS += $(BOOST_ROOT)
+	INCLUDE_DIRS += $(BOOST_ROOT)/include
 	BOOST_LDFLAGS+=-L$(BOOST_ROOT)/stage/lib -Wl,-rpath,$(BOOST_ROOT)/stage/lib
+	BOOST_LDFLAGS+=-L$(BOOST_ROOT)/lib -Wl,-rpath,$(BOOST_ROOT)/lib
 endif
 
 # BLAS configuration (default = MKL)
diff --git a/Makefile.config.example b/Makefile.config.example
index ce18c5cd2..25acbe9dc 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -79,11 +79,25 @@ USE_MKLDNN_AS_DEFAULT_ENGINE := 1
 # Use remove batch norm optimization to boost inference
 DISABLE_BN_FOLDING := 0
 
-#Use conv/eltwise/relu layer fusion to boost inference.
+# Use Conv + Relu fusion to boost inference
+DISABLE_CONV_RELU_FUSION:= 0
+
+# Use Bn + ReLU fusion to boost inference
+DISABLE_BN_RELU_FUSION := 0
+
+# Use Conv + Eltwise + Relu layer fusion to boost inference.
 DISABLE_CONV_SUM_FUSION := 0
+
+# Use sparse to boost inference.
+DISABLE_SPARSE := 0
+
+# Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) 
+# Uncomment to disable MKLDNN download by customized setting
+# DISABLE_MKLDNN_DOWNLOAD := 1
+
 # Intel(r) Machine Learning Scaling Library (uncomment to build
 # with MLSL for multi-node training)
-# USE_MLSL :=1
+USE_MLSL := 1
 
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda
diff --git a/Makefile.mkldnn b/Makefile.mkldnn
index d113a8923..f2dd30053 100644
--- a/Makefile.mkldnn
+++ b/Makefile.mkldnn
@@ -24,6 +24,17 @@ endif
 MKLDNN_GITHUB := https://github.com/01org/mkl-dnn.git
 MKLDNN_CMAKE_FLAGS += $(MKLDNN_SRCDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNN_INSTALLDIR) -DMKLROOT=${MKL_ROOTDIR} -B$(MKLDNN_BUILDDIR) -DCMAKE_CXX_COMPILER="$(MKLDNN_CXX)" -DCMAKE_C_COMPILER="$(MKLDNN_CC)"
 
+# Disable MKLDNN download by customized setting
+ifeq ($(DISABLE_MKLDNN_DOWNLOAD), 1)
+disable_mkldnn_download_info:
+	@echo "MKLDNN download is disabled by customized setting! If MKLDNNROOT is not set in the ENV, may cause compile error in the following steps!"
+mkldnn_download:
+mkldnn_build:
+
+else
+disable_mkldnn_download_info:
+	@echo "MKLDNN download is enabled by customized setting!"
+
 ifeq ("$(wildcard $(MKLDNN_INSTALLDIR)/include/mkldnn.hpp)", "")
 mkldnn_download:
 	git clone --no-checkout $(MKLDNN_GITHUB) $(MKLDNN_TMPDIR)
@@ -38,6 +49,7 @@ else
 mkldnn_download:
 mkldnn_build:
 endif
+endif
 
 mkldnn_clean:
 	@rm -rf $(MKLDNN_SRCDIR) $(MKLDNN_BUILDDIR) $(MKLDNN_INSTALLDIR) $(MKLDNN_TMPDIR)
@@ -55,10 +67,14 @@ mkldnnroot_notset: mkldnn_build
 	$(eval MKLDNN_LDFLAGS += -lmkldnn) \
 	$(eval MKLDNN_LDFLAGS += -L$(MKLDNN_INSTALLDIR)/lib -Wl,-rpath,${MKLDNN_INSTALLDIR}/lib)
 
+
+
 ifneq ($(origin MKLDNNROOT), undefined)
 ifdef MKLDNNROOT
 mkldnn: mkldnnroot_set
+mkldnn: disable_mkldnn_download_info
 endif
 else
 mkldnn: mkldnnroot_notset
+mkldnn: disable_mkldnn_download_info
 endif
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 732218865..bf8a75803 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -25,6 +25,11 @@ if(PERFORMANCE_MONITORING)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DPERFORMANCE_MONITORING")
 endif()
 
+# ---[ CPU DATA READONLY CO SIM
+if(CO_SIM)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DCO_SIM")
+endif()
+
 # ---[ Google-glog
 include("cmake/External/glog.cmake")
 include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
@@ -191,8 +196,10 @@ if(NOT APPLE)
 	set(MKL_INCLUDE_DIR "${MKL_ROOT_DIR}/include/")
 	if( ${MKL_EXTERNAL} EQUAL 1 )
 	  set(MKL_LIBRARIES "${MKL_ROOT_DIR}/lib/lib${MKL_LIBRARIES}.so")
+          set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${MKL_ROOT_DIR}/lib/)
 	else()
 	  set(MKL_LIBRARIES "${MKL_ROOT_DIR}/lib/intel64/lib${MKL_LIBRARIES}.so")
+          set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${MKL_ROOT_DIR}/lib/intel64/)
 	endif()
 	message(STATUS "Found MKL: ${MKL_INCLUDE_DIR}")
 	message(STATUS "Found MKL (include: ${MKL_INCLUDE_DIR}, lib: ${MKL_LIBRARIES}")	
@@ -237,6 +244,14 @@ endif()
 
 # ---[ MKLDNN
 if(DEFINED ENV{MKLDNNROOT})
+  if(DISABLE_MKLDNN_DOWNLOAD)
+    message(STATUS "MKLDNN download is disabled by customized setting!")
+    message(STATUS "MKLDNNROOT is already set in the ENV, will use ENV MKLDNNROOT in the following compile steps!")
+    add_definitions("-DDISABLE_MKLDNN_DOWNLOAD=1")
+  else()
+    message(STATUS "MKLDNN download is enabled by customized setting!")
+    add_definitions("-DDISABLE_MKLDNN_DOWNLOAD=0")
+  endif()
   set(MKLDNNROOT_DIR $ENV{MKLDNNROOT})
   if(NOT ${MKLDNNROOT_DIR} STREQUAL "")
     set(MKLDNNROOT_INCLUDE_DIR "${MKLDNNROOT_DIR}/include/")
@@ -249,6 +264,7 @@ if(DEFINED ENV{MKLDNNROOT})
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_MKLDNN_AS_DEFAULT_ENGINE")
       endif()
       list(APPEND Caffe_LINKER_LIBS "${MKLDNNROOT_DIR}/lib/libmkldnn.so")
+      set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${MKLDNNROOT_DIR}/lib/)
 	    include_directories(SYSTEM ${MKLDNNROOT_INCLUDE_DIR})
     else()
       message(STATUS "MKLDNN not found. MKLDNN_INCLUDE_DIR = ${MKLDNNROOT_INCLUDE_DIR}")
@@ -259,18 +275,27 @@ if(DEFINED ENV{MKLDNNROOT})
     endif()
   endif()
 else()
-  Download_MKLDNN()
+  if(DISABLE_MKLDNN_DOWNLOAD)
+    message(STATUS "MKLDNN download is disabled by customized setting!")
+    message(STATUS "MKLDNNROOT is not set in the ENV, will cause compile error in the following steps!")
+    add_definitions("-DDISABLE_MKLDNN_DOWNLOAD=1")
+  else()
+    message(STATUS "MKLDNN download is enabled by customized setting!")
+    add_definitions("-DDISABLE_MKLDNN_DOWNLOAD=0")
+    Download_MKLDNN()
 
-  message(STATUS "MKLDNN will be downloaded from github and installed in ${MKLDNN_INSTALL_DIR}")
-  message(STATUS "MKLDNN include directory: ${MKLDNN_INCLUDE_DIR}")
-  set(MKLDNN_SUPPORTED ON)
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DMKLDNN_SUPPORTED -std=c++11")
-  if(USE_MKLDNN_AS_DEFAULT_ENGINE)
-    message(STATUS "MKLDNN engine will be used as a default engine")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_MKLDNN_AS_DEFAULT_ENGINE")
+    message(STATUS "MKLDNN will be downloaded from github and installed in ${MKLDNN_INSTALL_DIR}")
+    message(STATUS "MKLDNN include directory: ${MKLDNN_INCLUDE_DIR}")
+    set(MKLDNN_SUPPORTED ON)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DMKLDNN_SUPPORTED -std=c++11")
+    if(USE_MKLDNN_AS_DEFAULT_ENGINE)
+      message(STATUS "MKLDNN engine will be used as a default engine")
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DUSE_MKLDNN_AS_DEFAULT_ENGINE")
+    endif()
+    list(APPEND Caffe_LINKER_LIBS "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so")
+    set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${MKLDNN_INSTALL_DIR}/lib/)
+      include_directories(${MKLDNN_INCLUDE_DIR})
   endif()
-  list(APPEND Caffe_LINKER_LIBS "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so")
-	include_directories(${MKLDNN_INCLUDE_DIR})
 endif()
 
 # ---[ Python
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index eaff48f7f..438e4b57b 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -11,11 +11,25 @@ if(DISABLE_BN_FOLDING)
 	add_definitions("-DDISABLE_BN_FOLDING")
 endif()
 
+if(DISABLE_CONV_RELU_FUSION)
+	message(STATUS "CONV + ReLU fusion is disabled!")
+	add_definitions("-DDISABLE_CONV_RELU_FUSION")
+endif()
+
+if(DISABLE_BN_RELU_FUSION)
+    message(STATUS "Bn + ReLU fusion is disabled!")
+    add_definitions("-DDISABLE_BN_RELU_FUSION")
+endif()
+
 if(DISABLE_CONV_SUM_FUSION)
 	message(STATUS "conv/eltwise/relu fusion is disabled!")
 	add_definitions("-DDISABLE_CONV_SUM_FUSION")
 endif()
 
+if(DISABLE_SPARSE)
+	message(STATUS "sparse is disabled!")
+	add_definitions("-DDISABLE_SPARSE")
+endif()
 # --[ If user doesn't specify build type then assume release
 if("${CMAKE_BUILD_TYPE}" STREQUAL "")
   set(CMAKE_BUILD_TYPE Release)
diff --git a/cmake/Modules/CodeCoverage.cmake b/cmake/Modules/CodeCoverage.cmake
new file mode 100644
index 000000000..ad70d4df9
--- /dev/null
+++ b/cmake/Modules/CodeCoverage.cmake
@@ -0,0 +1,19 @@
+include(CMakeParseArguments)
+
+find_program( GCOV gcov )
+if(NOT GCOV)
+    message(FATAL_ERROR "command gcov not found!")
+endif()
+
+set(CCOV_COMPILER_FLAGS "-g -O0 --coverage -fprofile-arcs -ftest-coverage"
+    CACHE INTERNAL "")
+
+if(NOT CMAKE_BUILD_TYPE STREQUAL "Debug")
+    message(WARNING "Non debug build may lead to wrong code coverage result.")
+endif()
+
+function(APPEND_CCOV_COMPILER_FLAGS)
+    set(CMAKE_C_FLAGS "${CMAKE_CXX_FLAGS} ${CCOV_COMPILER_FLAGS}" PARENT_SCOPE)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${CCOV_COMPILER_FLAGS}" PARENT_SCOPE)
+    message(STATUS "Setting code coverage compiler flags: ${CCOV_COMPILER_FLAGS}")
+endfunction()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index ec25efac2..787d7f722 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -121,6 +121,7 @@ function(caffe_print_configuration_summary)
   caffe_status("  ALLOW_LMDB_NOLOCK       :   ${ALLOW_LMDB_NOLOCK}")
   caffe_status("  USE_SYSTEMTAP           :   ${USE_SYSTEMTAP}")
   caffe_status("  PERFORMANCE_MONITORING  :   ${PERFORMANCE_MONITORING}")
+  caffe_status("  CO_SIM  :   ${CO_SIM}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
diff --git a/docker/standalone/cpu-ubuntu/Dockerfile b/docker/standalone/cpu-ubuntu/Dockerfile
index ae5fcb284..4418f133e 100644
--- a/docker/standalone/cpu-ubuntu/Dockerfile
+++ b/docker/standalone/cpu-ubuntu/Dockerfile
@@ -5,11 +5,22 @@ MAINTAINER caffe-maint@googlegroups.com
 #ENV https_proxy proxy:port
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
+	cpio \
         build-essential \
         cmake \
         git \
         wget \
         ssh \
+        openssh-server \
+        numactl \
+        vim \
+        net-tools \
+        iputils-ping \
+        screen \
+        libmlx4-1 libmlx5-1 ibutils  rdmacm-utils libibverbs1 ibverbs-utils perftest infiniband-diags \
+        openmpi-bin libopenmpi-dev \
+        ufw \
+        iptables \
         libboost-all-dev \
         libgflags-dev \
         libgoogle-glog-dev \
@@ -38,7 +49,7 @@ RUN pip install --upgrade pip
 RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/intel/caffe.git . && \
     for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
     mkdir build && cd build && \
-    cmake -DCPU_ONLY=1 -DCMAKE_BUILD_TYPE=Release .. && \
+    cmake -DCPU_ONLY=1 -DUSE_MLSL=1 -DCMAKE_BUILD_TYPE=Release .. && \
     make all -j"$(nproc)"
 
 ENV PYCAFFE_ROOT $CAFFE_ROOT/python
@@ -46,4 +57,19 @@ ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
 ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
 RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
 
-WORKDIR /workspace
+WORKDIR /opt/caffe
+
+RUN mkdir /var/run/sshd
+RUN echo 'root:123456' | chpasswd
+RUN sed -i 's/PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config
+RUN sed -i 's/Port 22/Port 10010/' /etc/ssh/sshd_config
+
+# SSH login fix. Otherwise user is kicked off after login
+RUN sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd
+
+ENV NOTVISIBLE "in users profile"
+RUN echo "export VISIBLE=now" >> /etc/profile
+
+EXPOSE 10010
+
+CMD ["/usr/sbin/sshd", "-D"]
diff --git a/examples/rfcn/README.md b/examples/rfcn/README.md
new file mode 100644
index 000000000..b93327ebf
--- /dev/null
+++ b/examples/rfcn/README.md
@@ -0,0 +1,238 @@
+# R-FCN
+R-FCN: Object Detection via Region-based Fully Convolutional Networks
+
+This is the cpu-only version of RFCN.
+
+### Disclaimer
+
+The official R-FCN code (written in MATLAB) is available [here](https://github.com/daijifeng001/R-FCN).
+
+R-FCN is modified from [the offcial py-R-FCN implementation](https://github.com/YuwenXiong/py-R-FCN), [the offcial R-FCN implementation](https://github.com/daijifeng001/R-FCN) and  [py-faster-rcnn code](https://github.com/rbgirshick/py-faster-rcnn ), and the usage is quite similar to [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn ).
+
+There are slight differences between py-R-FCN and the official R-FCN implementation.
+ - py-R-FCN is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 90ms / image vs. 99ms / image for ResNet-50)
+ - py-R-FCN supports both join training and alternative optimization of R-FCN.
+
+#### Some modification
+
+The original py-faster-rcnn uses class-aware bounding box regression. However, R-FCN use class-agnostic bounding box regression to reduce model complexity. So I add a configuration AGNOSTIC into fast_rcnn/config.py, and the default value is False. You should set it to True both on train and test phase if you want to use class-agnostic training and test. 
+
+OHEM need all rois to select the hard examples, so I changed the sample strategy, set `BATCH_SIZE: -1` for OHEM, otherwise OHEM would not take effect.
+
+In conclusion:
+
+`AGNOSTIC: True` is required for class-agnostic bounding box regression
+
+`BATCH_SIZE: -1` is required for OHEM
+
+And I've already provided two configuration files for you(w/ OHEM and w/o OHEM) under `experiments/cfgs` folder, you could just use them and needn't change anything.
+
+### License
+
+R-FCN is released under the MIT License (refer to the LICENSE file for details).
+
+### Citing R-FCN
+
+If you find R-FCN useful in your research, please consider citing:
+
+    @article{dai16rfcn,
+        Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
+        Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
+        Journal = {arXiv preprint arXiv:1605.06409},
+        Year = {2016}
+    }
+    
+### Main Results
+
+#### joint training
+
+|                   | training data       | test data             | mAP@0.5   | time/img (Titian X)|
+|-------------------|:-------------------:|:---------------------:|:-----:|:------------------:|
+|R-FCN, ResNet-50  | VOC 07+12 trainval  | VOC 07 test           | 77.6% | 0.099sec|           
+|R-FCN, ResNet-101 | VOC 07+12 trainval  | VOC 07 test           | 79.4% | 0.136sec|            
+
+|                   | training data       | test data             | mAP@[0.5:0.95]   | time/img (Titian X)|
+|-------------------|:-------------------:|:---------------------:|:-----:|:------------------:|
+|R-FCN, ResNet-101  | COCO 2014 train     | COCO 2014 val         | 27.9% | 0.138sec          |
+
+#### alternative optimization
+
+|                   | training data       | test data             | mAP@0.5   | time/img (Titian X)|
+|-------------------|:-------------------:|:---------------------:|:-----:|:------------------:|
+|R-FCN, ResNet-50  | VOC 07+12 trainval  | VOC 07 test           | 77.4%| 0.099sec            |
+|R-FCN, ResNet-101 | VOC 07+12 trainval  | VOC 07 test           | 79.4%| 0.136sec           |
+
+
+[VOC 0712 model (trained on VOC07+12 trainval) of R-FCN](https://1drv.ms/u/s!AoN7vygOjLIQqUWHpY67oaC7mopf)
+
+[COCO model (trained on 2014 train) of R-FCN](https://1drv.ms/u/s!AoN7vygOjLIQqiZEmKSodg7UudD4)
+
+
+### Requirements: software
+
+0. **`Important`** Please use the [Microsoft-version Caffe(@commit 1a2be8e)](https://github.com/Microsoft/caffe/tree/1a2be8ecf9ba318d516d79187845e90ac6e73197), this Caffe supports R-FCN layer, and the prototxt in this repository follows the Microsoft-version Caffe's layer name. You need to put the Caffe root folder under py-R-FCN folder, just like what py-faster-rcnn does.
+
+1. Requirements for `Caffe` and `pycaffe` (see: [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html))
+
+  **Note:** Caffe *must* be built with support for Python layers!
+
+  ```make
+  # In your Makefile.config, make sure to have this line uncommented
+  WITH_PYTHON_LAYER := 1
+  # Unrelatedly, it's also recommended that you use CUDNN
+  USE_CUDNN := 1
+  ```
+2. Python packages you might not have: `cython`, `opencv-python`, `easydict`
+3. [Optional] MATLAB is required for **official** PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.
+
+### Requirements: hardware
+
+Any NVIDIA GPU with 6GB or larger memory is OK(4GB is enough for ResNet-50).
+
+
+### Installation
+1. Clone the R-FCN repository
+  ```Shell
+  git clone https://github.com/Orpine/py-R-FCN.git
+  ```
+  We'll call the directory that you cloned R-FCN into `RFCN_ROOT`
+
+2. Clone the Caffe repository
+  ```Shell
+  cd $RFCN_ROOT
+  git clone https://github.com/Microsoft/caffe.git
+  ```
+  [optional] 
+  ```Shell
+  cd caffe
+  git reset --hard 1a2be8e
+  ```
+  (I only test on this commit, and I'm not sure whether this Caffe is still compatible with the prototxt in this repository in the future)
+  
+  If you followed the above instruction, python code will add `$RFCN_ROOT/caffe/python` to `PYTHONPATH` automatically, otherwise you need to add `$CAFFE_ROOT/python` by your own, you could check `$RFCN_ROOT/tools/_init_paths.py` for more details.
+
+3. Build the Cython modules
+    ```Shell
+    cd $RFCN_ROOT/lib
+    make
+    ```
+
+4. Build Caffe and pycaffe
+    ```Shell
+    cd $RFCN_ROOT/caffe
+    # Now follow the Caffe installation instructions here:
+    #   http://caffe.berkeleyvision.org/installation.html
+
+    # If you're experienced with Caffe and have all of the requirements installed
+    # and your Makefile.config in place, then simply do:
+    make -j8 && make pycaffe
+   ```
+
+### Demo
+1.  To use demo you need to download the pretrained R-FCN model, please download the model manually from [OneDrive](https://1drv.ms/u/s!AoN7vygOjLIQqUWHpY67oaC7mopf), and put it under `$RFCN/data`. 
+
+    Make sure it looks like this:
+    ```Shell
+    $RFCN/data/rfcn_models/resnet50_rfcn_final.caffemodel
+    $RFCN/data/rfcn_models/resnet101_rfcn_final.caffemodel
+    ```
+
+2.  To run the demo
+  
+    ```Shell
+    $RFCN/tools/demo_rfcn.py
+    ```
+    
+  The demo performs detection using a ResNet-101 network trained for detection on PASCAL VOC 2007.
+
+
+### Preparation for Training & Testing
+
+1. Download the training, validation, test data and VOCdevkit
+
+	```Shell
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
+	wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+	```
+
+2. Extract all of these tars into one directory named `VOCdevkit`
+
+	```Shell
+	tar xvf VOCtrainval_06-Nov-2007.tar
+	tar xvf VOCtest_06-Nov-2007.tar
+	tar xvf VOCdevkit_08-Jun-2007.tar
+	tar xvf VOCtrainval_11-May-2012.tar
+	```
+
+3. It should have this basic structure
+
+	```Shell
+  	$VOCdevkit/                           # development kit
+  	$VOCdevkit/VOCcode/                   # VOC utility code
+  	$VOCdevkit/VOC2007                    # image sets, annotations, etc.
+  	$VOCdevkit/VOC2012                    # image sets, annotations, etc.
+  	# ... and several other directories ...
+  	```
+
+4. Since py-faster-rcnn does not support multiple training datasets, we need to merge VOC 2007 data and VOC 2012 data manually. Just make a new directory named `VOC0712`, put all subfolders except `ImageSets` in `VOC2007` and `VOC2012` into `VOC0712`(you'll merge some folders). I provide a merged-version `ImageSets` folder for you, please put it into `VOCdevkit/VOC0712/`.
+
+5. Then the folder structure should look like this
+  ```Shell
+  	$VOCdevkit/                           # development kit
+  	$VOCdevkit/VOCcode/                   # VOC utility code
+  	$VOCdevkit/VOC2007                    # image sets, annotations, etc.
+  	$VOCdevkit/VOC2012                    # image sets, annotations, etc.
+  	$VOCdevkit/VOC0712                    # you just created this folder
+  	# ... and several other directories ...
+  ```
+
+4. Create symlinks for the PASCAL VOC dataset
+
+	```Shell
+    cd $RFCN_ROOT/data
+    ln -s $VOCdevkit VOCdevkit0712
+    ```
+
+5.  Please download ImageNet-pre-trained ResNet-50 and ResNet-100 model manually, and put them into `$RFCN_ROOT/data/imagenet_models`
+8.  Then everything is done, you could train your own model.
+
+### Usage
+
+To train and test a R-FCN detector using the **approximate joint training** method, use `experiments/scripts/rfcn_end2end.sh`.
+Output is written underneath `$RFCN_ROOT/output`.
+
+To train and test a R-FCN detector using the **approximate joint training** method **with OHEM**, use `experiments/scripts/rfcn_end2end_ohem.sh`.
+Output is written underneath `$RFCN_ROOT/output`.
+
+To train and test a R-FCN detector using the **alternative optimization** method **with OHEM**, use `experiments/scripts/rfcn_alt_opt_5stage_ohem.sh`.
+Output is written underneath `$RFCN_ROOT/output`
+
+```Shell
+cd $RFCN_ROOT
+./experiments/scripts/rfcn_end2end[_ohem].sh [GPU_ID] [NET] [DATASET] [--set ...]
+# GPU_ID is the GPU you want to train on
+# NET in {ResNet-50, ResNet-101} is the network arch to use
+# DATASET in {pascal_voc, coco} is the dataset to use(I only tested on pascal_voc)
+# --set ... allows you to specify fast_rcnn.config options, e.g.
+#   --set EXP_DIR seed_rng1701 RNG_SEED 1701
+```
+
+Trained R-FCN networks are saved under:
+
+```
+output/<experiment directory>/<dataset name>/
+```
+
+Test outputs are saved under:
+
+```
+output/<experiment directory>/<dataset name>/<network snapshot name>/
+```
+
+### Misc
+
+Tested on Ubuntu 14.04 with a Titan X / GTX1080 GPU and Intel Xeon CPU E5-2620 v2 @ 2.10GHz 
+
+py-faster-rcnn code can also work properly, but I do not add any other feature(such as ResNet and OHEM).
\ No newline at end of file
diff --git a/examples/rfcn/data/README.md b/examples/rfcn/data/README.md
new file mode 100644
index 000000000..2f01d3d71
--- /dev/null
+++ b/examples/rfcn/data/README.md
@@ -0,0 +1,69 @@
+This directory holds (*after you download them*):
+- Caffe models pre-trained on ImageNet
+- Faster R-CNN models
+- Symlinks to datasets
+
+To download Caffe models (ZF, VGG16) pre-trained on ImageNet, run:
+
+```
+./data/scripts/fetch_imagenet_models.sh
+```
+
+This script will populate `data/imagenet_models`.
+
+To download Faster R-CNN models trained on VOC 2007, run:
+
+```
+./data/scripts/fetch_faster_rcnn_models.sh
+```
+
+This script will populate `data/faster_rcnn_models`.
+
+In order to train and test with PASCAL VOC, you will need to establish symlinks.
+From the `data` directory (`cd data`):
+
+```
+# For VOC 2007
+ln -s /your/path/to/VOC2007/VOCdevkit VOCdevkit2007
+
+# For VOC 2012
+ln -s /your/path/to/VOC2012/VOCdevkit VOCdevkit2012
+```
+
+Install the MS COCO dataset at /path/to/coco
+
+```
+ln -s /path/to/coco coco
+```
+
+For COCO with Fast R-CNN, place object proposals under `coco_proposals` (inside
+the `data` directory). You can obtain proposals on COCO from Jan Hosang at
+https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/how-good-are-detection-proposals-really/.
+For COCO, using MCG is recommended over selective search. MCG boxes can be downloaded
+from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/.
+Use the tool `lib/datasets/tools/mcg_munge.py` to convert the downloaded MCG data
+into the same file layout as those from Jan Hosang.
+
+Since you'll likely be experimenting with multiple installs of Fast/er R-CNN in
+parallel, you'll probably want to keep all of this data in a shared place and
+use symlinks. On my system I create the following symlinks inside `data`:
+
+Annotations for the 5k image 'minival' subset of COCO val2014 that I like to use
+can be found at https://dl.dropboxusercontent.com/s/o43o90bna78omob/instances_minival2014.json.zip?dl=0.
+Annotations for COCO val2014 (set) minus minival (~35k images) can be found at
+https://dl.dropboxusercontent.com/s/s3tw5zcg7395368/instances_valminusminival2014.json.zip?dl=0.
+
+```
+# data/cache holds various outputs created by the datasets package
+ln -s /data/fast_rcnn_shared/cache
+
+# move the imagenet_models to shared location and symlink to them
+ln -s /data/fast_rcnn_shared/imagenet_models
+
+# move the selective search data to a shared location and symlink to them
+# (only applicable to Fast R-CNN training)
+ln -s /data/fast_rcnn_shared/selective_search_data
+
+ln -s /data/VOC2007/VOCdevkit VOCdevkit2007
+ln -s /data/VOC2012/VOCdevkit VOCdevkit2012
+```
diff --git a/examples/rfcn/data/demo/000456.jpg b/examples/rfcn/data/demo/000456.jpg
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diff --git a/examples/rfcn/data/scripts/fetch_faster_rcnn_models.sh b/examples/rfcn/data/scripts/fetch_faster_rcnn_models.sh
new file mode 100644
index 000000000..9b76fd56d
--- /dev/null
+++ b/examples/rfcn/data/scripts/fetch_faster_rcnn_models.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )"
+cd $DIR
+
+FILE=faster_rcnn_models.tgz
+URL=https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0
+CHECKSUM=ac116844f66aefe29587214272054668
+
+if [ -f $FILE ]; then
+  echo "File already exists. Checking md5..."
+  os=`uname -s`
+  if [ "$os" = "Linux" ]; then
+    checksum=`md5sum $FILE | awk '{ print $1 }'`
+  elif [ "$os" = "Darwin" ]; then
+    checksum=`cat $FILE | md5`
+  fi
+  if [ "$checksum" = "$CHECKSUM" ]; then
+    echo "Checksum is correct. No need to download."
+    exit 0
+  else
+    echo "Checksum is incorrect. Need to download again."
+  fi
+fi
+
+echo "Downloading Faster R-CNN demo models (695M)..."
+
+wget $URL -O $FILE
+
+echo "Unzipping..."
+
+tar zxvf $FILE
+
+echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/rfcn/data/scripts/fetch_imagenet_models.sh b/examples/rfcn/data/scripts/fetch_imagenet_models.sh
new file mode 100644
index 000000000..46835b9fb
--- /dev/null
+++ b/examples/rfcn/data/scripts/fetch_imagenet_models.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )"
+cd $DIR
+
+FILE=imagenet_models.tgz
+URL=https://dl.dropbox.com/s/gstw7122padlf0l/imagenet_models.tgz?dl=0
+CHECKSUM=ed34ca912d6782edfb673a8c3a0bda6d
+
+if [ -f $FILE ]; then
+  echo "File already exists. Checking md5..."
+  os=`uname -s`
+  if [ "$os" = "Linux" ]; then
+    checksum=`md5sum $FILE | awk '{ print $1 }'`
+  elif [ "$os" = "Darwin" ]; then
+    checksum=`cat $FILE | md5`
+  fi
+  if [ "$checksum" = "$CHECKSUM" ]; then
+    echo "Checksum is correct. No need to download."
+    exit 0
+  else
+    echo "Checksum is incorrect. Need to download again."
+  fi
+fi
+
+echo "Downloading pretrained ImageNet models (1G)..."
+
+wget $URL -O $FILE
+
+echo "Unzipping..."
+
+tar zxvf $FILE
+
+echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/rfcn/data/scripts/fetch_selective_search_data.sh b/examples/rfcn/data/scripts/fetch_selective_search_data.sh
new file mode 100644
index 000000000..bf925fc5b
--- /dev/null
+++ b/examples/rfcn/data/scripts/fetch_selective_search_data.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../" && pwd )"
+cd $DIR
+
+FILE=selective_search_data.tgz
+URL=https://dl.dropboxusercontent.com/s/orrt7o6bp6ae0tc/selective_search_data.tgz?dl=0
+CHECKSUM=7078c1db87a7851b31966b96774cd9b9
+
+if [ -f $FILE ]; then
+  echo "File already exists. Checking md5..."
+  os=`uname -s`
+  if [ "$os" = "Linux" ]; then
+    checksum=`md5sum $FILE | awk '{ print $1 }'`
+  elif [ "$os" = "Darwin" ]; then
+    checksum=`cat $FILE | md5`
+  fi
+  if [ "$checksum" = "$CHECKSUM" ]; then
+    echo "Checksum is correct. No need to download."
+    exit 0
+  else
+    echo "Checksum is incorrect. Need to download again."
+  fi
+fi
+
+echo "Downloading precomputed selective search boxes (0.5G)..."
+
+wget $URL -O $FILE
+
+echo "Unzipping..."
+
+tar zxvf $FILE
+
+echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/rfcn/experiments/README.md b/examples/rfcn/experiments/README.md
new file mode 100644
index 000000000..076257dac
--- /dev/null
+++ b/examples/rfcn/experiments/README.md
@@ -0,0 +1,5 @@
+Scripts are under `experiments/scripts`.
+
+Each script saves a log file under `experiments/logs`.
+
+Configuration override files used in the experiments are stored in `experiments/cfgs`.
diff --git a/examples/rfcn/experiments/cfgs/faster_rcnn_alt_opt.yml b/examples/rfcn/experiments/cfgs/faster_rcnn_alt_opt.yml
new file mode 100644
index 000000000..9f11bd60d
--- /dev/null
+++ b/examples/rfcn/experiments/cfgs/faster_rcnn_alt_opt.yml
@@ -0,0 +1,5 @@
+EXP_DIR: faster_rcnn_alt_opt
+TRAIN:
+  BG_THRESH_LO: 0.0
+TEST:
+  HAS_RPN: True
diff --git a/examples/rfcn/experiments/cfgs/faster_rcnn_end2end.yml b/examples/rfcn/experiments/cfgs/faster_rcnn_end2end.yml
new file mode 100644
index 000000000..4c6a0545d
--- /dev/null
+++ b/examples/rfcn/experiments/cfgs/faster_rcnn_end2end.yml
@@ -0,0 +1,11 @@
+EXP_DIR: faster_rcnn_end2end
+TRAIN:
+  HAS_RPN: True
+  IMS_PER_BATCH: 1
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+TEST:
+  HAS_RPN: True
diff --git a/examples/rfcn/experiments/cfgs/rfcn_alt_opt_5step_ohem.yml b/examples/rfcn/experiments/cfgs/rfcn_alt_opt_5step_ohem.yml
new file mode 100644
index 000000000..9e96f35bd
--- /dev/null
+++ b/examples/rfcn/experiments/cfgs/rfcn_alt_opt_5step_ohem.yml
@@ -0,0 +1,12 @@
+EXP_DIR: rfcn_alt_opt_5step_ohem
+TRAIN:
+  BG_THRESH_LO: 0.0
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 300
+  AGNOSTIC: True
+  BATCH_SIZE: -1
+  RPN_NORMALIZE_TARGETS: True
+TEST:
+  PROPOSAL_METHOD: 'rpn'
+  HAS_RPN: False
+  AGNOSTIC: True
diff --git a/examples/rfcn/experiments/cfgs/rfcn_end2end.yml b/examples/rfcn/experiments/cfgs/rfcn_end2end.yml
new file mode 100644
index 000000000..992ae3c9f
--- /dev/null
+++ b/examples/rfcn/experiments/cfgs/rfcn_end2end.yml
@@ -0,0 +1,17 @@
+EXP_DIR: rfcn_end2end
+TRAIN:
+  HAS_RPN: True
+  IMS_PER_BATCH: 1
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.1
+  BATCH_SIZE: 128
+  AGNOSTIC: True
+  SNAPSHOT_ITERS: 10000
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 300
+TEST:
+  HAS_RPN: True
+  AGNOSTIC: True
diff --git a/examples/rfcn/experiments/cfgs/rfcn_end2end_ohem.yml b/examples/rfcn/experiments/cfgs/rfcn_end2end_ohem.yml
new file mode 100644
index 000000000..240561e92
--- /dev/null
+++ b/examples/rfcn/experiments/cfgs/rfcn_end2end_ohem.yml
@@ -0,0 +1,18 @@
+EXP_DIR: rfcn_end2end_ohem
+TRAIN:
+  HAS_RPN: True
+  IMS_PER_BATCH: 1
+  BBOX_NORMALIZE_TARGETS_PRECOMPUTED: True
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_NORMALIZE_TARGETS: True
+  RPN_BATCHSIZE: 256
+  PROPOSAL_METHOD: gt
+  BG_THRESH_LO: 0.0
+  BATCH_SIZE: -1
+  AGNOSTIC: True
+  SNAPSHOT_ITERS: 10000
+  RPN_PRE_NMS_TOP_N: 6000
+  RPN_POST_NMS_TOP_N: 300
+TEST:
+  HAS_RPN: True
+  AGNOSTIC: True
diff --git a/examples/rfcn/experiments/scripts/rfcn_alt_opt_5stage_ohem.sh b/examples/rfcn/experiments/scripts/rfcn_alt_opt_5stage_ohem.sh
new file mode 100755
index 000000000..f9ee0ea1e
--- /dev/null
+++ b/examples/rfcn/experiments/scripts/rfcn_alt_opt_5stage_ohem.sh
@@ -0,0 +1,70 @@
+#!/bin/bash
+# Usage:
+# ./experiments/scripts/faster_rcnn_alt_opt.sh GPU NET DATASET [options args to {train,test}_net.py]
+# DATASET is only pascal_voc for now
+#
+# Example:
+# ./experiments/scripts/faster_rcnn_alt_opt.sh 0 VGG_CNN_M_1024 pascal_voc \
+#   --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400, 500, 600, 700]"
+
+set -x
+set -e
+
+export PYTHONUNBUFFERED="True"
+
+GPU_ID=$1
+NET=$2
+NET_lc=${NET,,}
+DATASET=$3
+
+array=( $@ )
+len=${#array[@]}
+EXTRA_ARGS=${array[@]:3:$len}
+EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_}
+
+case $DATASET in
+  pascal_voc)
+    TRAIN_IMDB="voc_0712_trainval"
+    TEST_IMDB="voc_0712_test"
+    PT_DIR="pascal_voc"
+    ;;
+  coco)
+    TRAIN_IMDB="coco_2014_train"
+    TEST_IMDB="coco_2014_val"
+    PT_DIR="coco"
+    ITERS=40000
+    ;;
+  *)
+    echo "No dataset given"
+    exit
+    ;;
+esac
+
+LOG="experiments/logs/rfcn_alt_opt_5step_ohem_rpn4_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`"
+if [[ ! -e "experiments/logs" ]]; then
+    mkdir experiments/logs
+fi
+exec &> >(tee -a "$LOG")
+echo Logging output to "$LOG"
+
+time ./tools/train_rfcn_alt_opt_5stage.py --gpu ${GPU_ID} \
+  --net_name ${NET} \
+  --weights data/imagenet_models/${NET}-model.caffemodel \
+  --imdb ${TRAIN_IMDB} \
+  --imdb_test ${TEST_IMDB} \
+  --cfg experiments/cfgs/rfcn_alt_opt_5step_ohem.yml \
+  --model 'rfcn_alt_opt_5step_ohem'
+  ${EXTRA_ARGS}
+
+set +x
+NET_FINAL=`grep "Final model" ${LOG} | awk '{print $3}'`
+RPN_FINAL=`grep "Final RPN" ${LOG} | awk '{print $3}'`
+set -x
+time ./tools/test_net.py --gpu ${GPU_ID} \
+  --def models/${PT_DIR}/${NET}/rfcn_alt_opt_5step_ohem/rfcn_test.pt \
+  --net ${NET_FINAL} \
+  --imdb ${TEST_IMDB} \
+  --rpn_file ${RPN_FINAL} \
+  --cfg experiments/cfgs/rfcn_alt_opt_5step_ohem.yml \
+  --num_dets 400
+  ${EXTRA_ARGS}
diff --git a/examples/rfcn/experiments/scripts/rfcn_end2end.sh b/examples/rfcn/experiments/scripts/rfcn_end2end.sh
new file mode 100755
index 000000000..85070ebe7
--- /dev/null
+++ b/examples/rfcn/experiments/scripts/rfcn_end2end.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+# Usage:
+# ./experiments/scripts/rfcn_end2end.sh GPU NET DATASET [options args to {train,test}_net.py]
+# DATASET is either pascal_voc or coco.
+#
+# Example:
+# ./experiments/scripts/rfcn_end2end.sh 0 ResNet50 pascal_voc \
+#   --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400, 500, 600, 700]"
+
+set -x
+set -e
+
+export PYTHONUNBUFFERED="True"
+
+# GPU_ID=$1
+NET=$2
+NET_lc=${NET,,}
+DATASET=$3
+
+array=( $@ )
+len=${#array[@]}
+EXTRA_ARGS=${array[@]:3:$len}
+EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_}
+
+case $DATASET in
+  pascal_voc)
+    TRAIN_IMDB="voc_0712_trainval"
+    TEST_IMDB="voc_0712_test"
+    PT_DIR="pascal_voc"
+    ITERS=110000
+    ;;
+  coco)
+    # This is a very long and slow training schedule
+    # You can probably use fewer iterations and reduce the
+    # time to the LR drop (set in the solver to 350,000 iterations).
+    TRAIN_IMDB="coco_2014_train"
+    TEST_IMDB="coco_2014_val"
+    PT_DIR="coco"
+    ITERS=960000
+    ;;
+  luna)
+    # This is a very long and slow training schedule
+    # You can probably use fewer iterations and reduce the
+    # time to the LR drop (set in the solver to 350,000 iterations).
+    TRAIN_IMDB="luna_2016_train"
+    TEST_IMDB="luna_2016_val"
+    PT_DIR="luna"
+    ITERS=960000
+    ;;
+  *)
+    echo "No dataset given"
+    exit
+    ;;
+esac
+
+LOG="experiments/logs/rfcn_end2end_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`"
+
+if [[ ! -e "experiments/logs" ]]; then
+    mkdir experiments/logs
+fi
+
+exec &> >(tee -a "$LOG")
+echo Logging output to "$LOG"
+
+time ./tools/train_net.py  \
+  --solver ../../models/intel_optimized_models/rfcn/${PT_DIR}/${NET}/rfcn_end2end/solver.prototxt \
+  --imdb ${TRAIN_IMDB} \
+  --weights data/imagenet_models/${NET}-model.caffemodel \
+  --iters ${ITERS} \
+  --cfg experiments/cfgs/rfcn_end2end.yml \
+  ${EXTRA_ARGS}
+
+set +x
+NET_FINAL=`grep -B 1 "done solving" ${LOG} | grep "Wrote snapshot" | awk '{print $4}'`
+set -x
+
+time ./tools/test_net.py  \
+  --def ../../models/intel_optimized_models/rfcn/${PT_DIR}/${NET}/rfcn_end2end/test_agnostic.prototxt \
+  --net ${NET_FINAL} \
+  --imdb ${TEST_IMDB} \
+  --cfg experiments/cfgs/rfcn_end2end.yml \
+  ${EXTRA_ARGS}
diff --git a/examples/rfcn/experiments/scripts/rfcn_end2end_ohem.sh b/examples/rfcn/experiments/scripts/rfcn_end2end_ohem.sh
new file mode 100755
index 000000000..df20e5f79
--- /dev/null
+++ b/examples/rfcn/experiments/scripts/rfcn_end2end_ohem.sh
@@ -0,0 +1,75 @@
+#!/bin/bash
+# Usage:
+# ./experiments/scripts/rfcn_end2end_ohem.sh GPU NET DATASET [options args to {train,test}_net.py]
+# DATASET is either pascal_voc or coco.
+#
+# Example:
+# ./experiments/scripts/rfcn_end2end_ohem.sh 0 ResNet50 pascal_voc \
+#   --set EXP_DIR foobar RNG_SEED 42 TRAIN.SCALES "[400, 500, 600, 700]"
+
+set -x
+set -e
+
+export PYTHONUNBUFFERED="True"
+
+# GPU_ID=$1
+NET=$2
+NET_lc=${NET,,}
+DATASET=$3
+
+array=( $@ )
+len=${#array[@]}
+EXTRA_ARGS=${array[@]:3:$len}
+EXTRA_ARGS_SLUG=${EXTRA_ARGS// /_}
+
+case $DATASET in
+  pascal_voc)
+    TRAIN_IMDB="voc_0712_trainval"
+    TEST_IMDB="voc_0712_test"
+    PT_DIR="pascal_voc"
+    ITERS=110000
+    ;;
+  coco)
+    # This is a very long and slow training schedule
+    # You can probably use fewer iterations and reduce the
+    # time to the LR drop (set in the solver to 350,000 iterations).
+    TRAIN_IMDB="coco_2014_train"
+    TEST_IMDB="coco_2014_val"
+    PT_DIR="coco"
+    ITERS=960000
+    ;;
+  *)
+    echo "No dataset given"
+    exit
+    ;;
+esac
+
+LOG="experiments/logs/rfcn_end2end_${NET}_${EXTRA_ARGS_SLUG}.txt.`date +'%Y-%m-%d_%H-%M-%S'`"
+
+if [[ ! -e "experiments/logs" ]]; then
+    mkdir experiments/logs
+fi
+
+exec &> >(tee -a "$LOG")
+echo Logging output to "$LOG"
+
+
+time ./tools/train_net.py \
+  --solver ../../models/intel_optimized_models/faster-rcnn/${PT_DIR}/${NET}/rfcn_end2end/solver_ohem.prototxt \
+  --weights data/imagenet_models/${NET}-model.caffemodel \
+  --imdb ${TRAIN_IMDB} \
+  --iters ${ITERS} \
+  --cfg experiments/cfgs/rfcn_end2end_ohem.yml \
+  ${EXTRA_ARGS}
+
+
+set +x
+NET_FINAL=`tail -n 100 ${LOG} | grep -B 1 "done solving" | grep "Wrote snapshot" | awk '{print $4}'`
+set -x
+
+time ./tools/test_net.py --gpu ${GPU_ID} \
+  --def ../../models/intel_optimized_models/faster-rcnn/${PT_DIR}/${NET}/rfcn_end2end/test_agnostic.prototxt \
+  --net ${NET_FINAL} \
+  --imdb ${TEST_IMDB} \
+  --cfg experiments/cfgs/rfcn_end2end_ohem.yml \
+  ${EXTRA_ARGS}
diff --git a/examples/rfcn/lib/Makefile b/examples/rfcn/lib/Makefile
new file mode 100644
index 000000000..a48239850
--- /dev/null
+++ b/examples/rfcn/lib/Makefile
@@ -0,0 +1,3 @@
+all:
+	python setup.py build_ext --inplace
+	rm -rf build
diff --git a/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m
new file mode 100644
index 000000000..629597a1f
--- /dev/null
+++ b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/get_voc_opts.m
@@ -0,0 +1,14 @@
+function VOCopts = get_voc_opts(path)
+
+tmp = pwd;
+cd(path);
+try
+  addpath('VOCcode');
+  VOCinit;
+catch
+  rmpath('VOCcode');
+  cd(tmp);
+  error(sprintf('VOCcode directory not found under %s', path));
+end
+rmpath('VOCcode');
+cd(tmp);
diff --git a/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m
new file mode 100644
index 000000000..1911a0e39
--- /dev/null
+++ b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m
@@ -0,0 +1,56 @@
+function res = voc_eval(path, comp_id, test_set, output_dir)
+
+VOCopts = get_voc_opts(path);
+VOCopts.testset = test_set;
+
+for i = 1:length(VOCopts.classes)
+  cls = VOCopts.classes{i};
+  res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
+end
+
+fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
+fprintf('Results:\n');
+aps = [res(:).ap]';
+fprintf('%.1f\n', aps * 100);
+fprintf('%.1f\n', mean(aps) * 100);
+fprintf('~~~~~~~~~~~~~~~~~~~~\n');
+
+function res = voc_eval_cls(cls, VOCopts, comp_id, output_dir)
+
+test_set = VOCopts.testset;
+year = VOCopts.dataset(4:end);
+
+addpath(fullfile(VOCopts.datadir, 'VOCcode'));
+
+res_fn = sprintf(VOCopts.detrespath, comp_id, cls);
+
+recall = [];
+prec = [];
+ap = 0;
+ap_auc = 0;
+
+do_eval = (str2num(year) <= 2007) | ~strcmp(test_set, 'test');
+if do_eval
+  % Bug in VOCevaldet requires that tic has been called first
+  tic;
+  [recall, prec, ap] = VOCevaldet(VOCopts, comp_id, cls, true);
+  ap_auc = xVOCap(recall, prec);
+
+  % force plot limits
+  ylim([0 1]);
+  xlim([0 1]);
+
+  print(gcf, '-djpeg', '-r0', ...
+        [output_dir '/' cls '_pr.jpg']);
+end
+fprintf('!!! %s : %.4f %.4f\n', cls, ap, ap_auc);
+
+res.recall = recall;
+res.prec = prec;
+res.ap = ap;
+res.ap_auc = ap_auc;
+
+save([output_dir '/' cls '_pr.mat'], ...
+     'res', 'recall', 'prec', 'ap', 'ap_auc');
+
+rmpath(fullfile(VOCopts.datadir, 'VOCcode'));
diff --git a/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m
new file mode 100644
index 000000000..de6c6283c
--- /dev/null
+++ b/examples/rfcn/lib/datasets/VOCdevkit-matlab-wrapper/xVOCap.m
@@ -0,0 +1,10 @@
+function ap = xVOCap(rec,prec)
+% From the PASCAL VOC 2011 devkit
+
+mrec=[0 ; rec ; 1];
+mpre=[0 ; prec ; 0];
+for i=numel(mpre)-1:-1:1
+    mpre(i)=max(mpre(i),mpre(i+1));
+end
+i=find(mrec(2:end)~=mrec(1:end-1))+1;
+ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
diff --git a/examples/rfcn/lib/datasets/__init__.py b/examples/rfcn/lib/datasets/__init__.py
new file mode 100644
index 000000000..7ba6a65c5
--- /dev/null
+++ b/examples/rfcn/lib/datasets/__init__.py
@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
diff --git a/examples/rfcn/lib/datasets/coco.py b/examples/rfcn/lib/datasets/coco.py
new file mode 100644
index 000000000..00b2954bc
--- /dev/null
+++ b/examples/rfcn/lib/datasets/coco.py
@@ -0,0 +1,414 @@
+# --------------------------------------------------------
+# Fast/er R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+from datasets.imdb import imdb
+import datasets.ds_utils as ds_utils
+from fast_rcnn.config import cfg
+import os.path as osp
+import sys
+import os
+import numpy as np
+import scipy.sparse
+import scipy.io as sio
+import cPickle
+import json
+import uuid
+# COCO API
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from pycocotools import mask as COCOmask
+
+def _filter_crowd_proposals(roidb, crowd_thresh):
+    """
+    Finds proposals that are inside crowd regions and marks them with
+    overlap = -1 (for all gt rois), which means they will be excluded from
+    training.
+    """
+    for ix, entry in enumerate(roidb):
+        overlaps = entry['gt_overlaps'].toarray()
+        crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
+        non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
+        if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
+            continue
+        iscrowd = [int(True) for _ in xrange(len(crowd_inds))]
+        crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
+        non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
+        ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
+        bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
+        overlaps[non_gt_inds[bad_inds], :] = -1
+        roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
+    return roidb
+
+class coco(imdb):
+    def __init__(self, image_set, year):
+        imdb.__init__(self, 'coco_' + year + '_' + image_set)
+        # COCO specific config options
+        self.config = {'top_k' : 2000,
+                       'use_salt' : True,
+                       'cleanup' : True,
+                       'crowd_thresh' : 0.7,
+                       'rpn_file': None,
+                       'min_size' : 2}
+        # name, paths
+        self._year = year
+        self._image_set = image_set
+        self._data_path = osp.join(cfg.DATA_DIR, 'coco')
+        # load COCO API, classes, class <-> id mappings
+        self._COCO = COCO(self._get_ann_file())
+        cats = self._COCO.loadCats(self._COCO.getCatIds())
+        self._classes = tuple(['__background__'] + [c['name'] for c in cats])
+        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
+        self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
+                                              self._COCO.getCatIds()))
+        self._image_index = self._load_image_set_index()
+        # Default to roidb handler
+        self.set_proposal_method('selective_search')
+        self.competition_mode(False)
+
+        # Some image sets are "views" (i.e. subsets) into others.
+        # For example, minival2014 is a random 5000 image subset of val2014.
+        # This mapping tells us where the view's images and proposals come from.
+        self._view_map = {
+            'minival2014' : 'val2014',          # 5k val2014 subset
+            'valminusminival2014' : 'val2014',  # val2014 \setminus minival2014
+        }
+        coco_name = image_set + year  # e.g., "val2014"
+        self._data_name = (self._view_map[coco_name]
+                           if self._view_map.has_key(coco_name)
+                           else coco_name)
+        # Dataset splits that have ground-truth annotations (test splits
+        # do not have gt annotations)
+        self._gt_splits = ('train', 'val', 'minival')
+
+    def _get_ann_file(self):
+        prefix = 'instances' if self._image_set.find('test') == -1 \
+                             else 'image_info'
+        return osp.join(self._data_path, 'annotations',
+                        prefix + '_' + self._image_set + self._year + '.json')
+
+    def _load_image_set_index(self):
+        """
+        Load image ids.
+        """
+        image_ids = self._COCO.getImgIds()
+        return image_ids
+
+    def _get_widths(self):
+        anns = self._COCO.loadImgs(self._image_index)
+        widths = [ann['width'] for ann in anns]
+        return widths
+
+    def image_path_at(self, i):
+        """
+        Return the absolute path to image i in the image sequence.
+        """
+        return self.image_path_from_index(self._image_index[i])
+
+    def image_path_from_index(self, index):
+        """
+        Construct an image path from the image's "index" identifier.
+        """
+        # Example image path for index=119993:
+        #   images/train2014/COCO_train2014_000000119993.jpg
+        file_name = ('COCO_' + self._data_name + '_' +
+                     str(index).zfill(12) + '.jpg')
+        image_path = osp.join(self._data_path, 'images',
+                              self._data_name, file_name)
+        assert osp.exists(image_path), \
+                'Path does not exist: {}'.format(image_path)
+        return image_path
+
+    def selective_search_roidb(self):
+        return self._roidb_from_proposals('selective_search')
+
+    def edge_boxes_roidb(self):
+        return self._roidb_from_proposals('edge_boxes_AR')
+
+    def mcg_roidb(self):
+        return self._roidb_from_proposals('MCG')
+
+    def rpn_roidb(self):
+        if (self._image_set != 'val') and ('test' not in self._image_set):
+            gt_roidb = self.gt_roidb()
+            rpn_roidb = self._load_rpn_roidb(gt_roidb)
+            roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
+        else:
+            roidb = self._load_rpn_roidb(None)
+
+        return roidb
+
+    def _load_rpn_roidb(self, gt_roidb):
+        filename = self.config['rpn_file']
+        print 'loading {}'.format(filename)
+        assert os.path.exists(filename), \
+               'rpn data not found at: {}'.format(filename)
+        with open(filename, 'rb') as f:
+            box_list = cPickle.load(f)
+        return self.create_roidb_from_box_list(box_list, gt_roidb)
+
+    def _roidb_from_proposals(self, method):
+        """
+        Creates a roidb from pre-computed proposals of a particular methods.
+        """
+        top_k = self.config['top_k']
+        cache_file = osp.join(self.cache_path, self.name +
+                              '_{:s}_top{:d}'.format(method, top_k) +
+                              '_roidb.pkl')
+
+        if osp.exists(cache_file):
+            with open(cache_file, 'rb') as fid:
+                roidb = cPickle.load(fid)
+            print '{:s} {:s} roidb loaded from {:s}'.format(self.name, method,
+                                                            cache_file)
+            return roidb
+
+        if self._image_set in self._gt_splits:
+            gt_roidb = self.gt_roidb()
+            method_roidb = self._load_proposals(method, gt_roidb)
+            roidb = imdb.merge_roidbs(gt_roidb, method_roidb)
+            # Make sure we don't use proposals that are contained in crowds
+            roidb = _filter_crowd_proposals(roidb, self.config['crowd_thresh'])
+        else:
+            roidb = self._load_proposals(method, None)
+        with open(cache_file, 'wb') as fid:
+            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'wrote {:s} roidb to {:s}'.format(method, cache_file)
+        return roidb
+
+    def _load_proposals(self, method, gt_roidb):
+        """
+        Load pre-computed proposals in the format provided by Jan Hosang:
+        http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
+          computing/research/object-recognition-and-scene-understanding/how-
+          good-are-detection-proposals-really/
+        For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
+          CS/vision/grouping/mcg/ and convert the file layout using
+        lib/datasets/tools/mcg_munge.py.
+        """
+        box_list = []
+        top_k = self.config['top_k']
+        valid_methods = [
+            'MCG',
+            'selective_search',
+            'edge_boxes_AR',
+            'edge_boxes_70']
+        assert method in valid_methods
+
+        print 'Loading {} boxes'.format(method)
+        for i, index in enumerate(self._image_index):
+            if i % 1000 == 0:
+                print '{:d} / {:d}'.format(i + 1, len(self._image_index))
+
+            box_file = osp.join(
+                cfg.DATA_DIR, 'coco_proposals', method, 'mat',
+                self._get_box_file(index))
+
+            raw_data = sio.loadmat(box_file)['boxes']
+            boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
+            if method == 'MCG':
+                # Boxes from the MCG website are in (y1, x1, y2, x2) order
+                boxes = boxes[:, (1, 0, 3, 2)]
+            # Remove duplicate boxes and very small boxes and then take top k
+            keep = ds_utils.unique_boxes(boxes)
+            boxes = boxes[keep, :]
+            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
+            boxes = boxes[keep, :]
+            boxes = boxes[:top_k, :]
+            box_list.append(boxes)
+            # Sanity check
+            im_ann = self._COCO.loadImgs(index)[0]
+            width = im_ann['width']
+            height = im_ann['height']
+            ds_utils.validate_boxes(boxes, width=width, height=height)
+        return self.create_roidb_from_box_list(box_list, gt_roidb)
+
+    def gt_roidb(self):
+        """
+        Return the database of ground-truth regions of interest.
+        This function loads/saves from/to a cache file to speed up future calls.
+        """
+        cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
+        if osp.exists(cache_file):
+            with open(cache_file, 'rb') as fid:
+                roidb = cPickle.load(fid)
+            print '{} gt roidb loaded from {}'.format(self.name, cache_file)
+            return roidb
+
+        gt_roidb = [self._load_coco_annotation(index)
+                    for index in self._image_index]
+
+        with open(cache_file, 'wb') as fid:
+            cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'wrote gt roidb to {}'.format(cache_file)
+        return gt_roidb
+
+    def _load_coco_annotation(self, index):
+        """
+        Loads COCO bounding-box instance annotations. Crowd instances are
+        handled by marking their overlaps (with all categories) to -1. This
+        overlap value means that crowd "instances" are excluded from training.
+        """
+        im_ann = self._COCO.loadImgs(index)[0]
+        width = im_ann['width']
+        height = im_ann['height']
+
+        annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
+        objs = self._COCO.loadAnns(annIds)
+        # Sanitize bboxes -- some are invalid
+        valid_objs = []
+        for obj in objs:
+            x1 = np.max((0, obj['bbox'][0]))
+            y1 = np.max((0, obj['bbox'][1]))
+            x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
+            y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
+            if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
+                obj['clean_bbox'] = [x1, y1, x2, y2]
+                valid_objs.append(obj)
+        objs = valid_objs
+        num_objs = len(objs)
+
+        boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+        gt_classes = np.zeros((num_objs), dtype=np.int32)
+        overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
+        seg_areas = np.zeros((num_objs), dtype=np.float32)
+
+        # Lookup table to map from COCO category ids to our internal class
+        # indices
+        coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
+                                          self._class_to_ind[cls])
+                                         for cls in self._classes[1:]])
+
+        for ix, obj in enumerate(objs):
+            cls = coco_cat_id_to_class_ind[obj['category_id']]
+            boxes[ix, :] = obj['clean_bbox']
+            gt_classes[ix] = cls
+            seg_areas[ix] = obj['area']
+            if obj['iscrowd']:
+                # Set overlap to -1 for all classes for crowd objects
+                # so they will be excluded during training
+                overlaps[ix, :] = -1.0
+            else:
+                overlaps[ix, cls] = 1.0
+
+        ds_utils.validate_boxes(boxes, width=width, height=height)
+        overlaps = scipy.sparse.csr_matrix(overlaps)
+        return {'boxes' : boxes,
+                'gt_classes': gt_classes,
+                'gt_overlaps' : overlaps,
+                'flipped' : False,
+                'seg_areas' : seg_areas}
+
+    def _get_box_file(self, index):
+        # first 14 chars / first 22 chars / all chars + .mat
+        # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
+        file_name = ('COCO_' + self._data_name +
+                     '_' + str(index).zfill(12) + '.mat')
+        return osp.join(file_name[:14], file_name[:22], file_name)
+
+    def _print_detection_eval_metrics(self, coco_eval):
+        IoU_lo_thresh = 0.5
+        IoU_hi_thresh = 0.95
+        def _get_thr_ind(coco_eval, thr):
+            ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) &
+                           (coco_eval.params.iouThrs < thr + 1e-5))[0][0]
+            iou_thr = coco_eval.params.iouThrs[ind]
+            assert np.isclose(iou_thr, thr)
+            return ind
+
+        ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh)
+        ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh)
+        # precision has dims (iou, recall, cls, area range, max dets)
+        # area range index 0: all area ranges
+        # max dets index 2: 100 per image
+        precision = \
+            coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
+        ap_default = np.mean(precision[precision > -1])
+        print ('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] '
+               '~~~~').format(IoU_lo_thresh, IoU_hi_thresh)
+        print '{:.1f}'.format(100 * ap_default)
+        for cls_ind, cls in enumerate(self.classes):
+            if cls == '__background__':
+                continue
+            # minus 1 because of __background__
+            precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2]
+            ap = np.mean(precision[precision > -1])
+            print '{:.1f}'.format(100 * ap)
+
+        print '~~~~ Summary metrics ~~~~'
+        coco_eval.summarize()
+
+    def _do_detection_eval(self, res_file, output_dir):
+        ann_type = 'bbox'
+        coco_dt = self._COCO.loadRes(res_file)
+        coco_eval = COCOeval(self._COCO, coco_dt)
+        coco_eval.params.useSegm = (ann_type == 'segm')
+        coco_eval.evaluate()
+        coco_eval.accumulate()
+        self._print_detection_eval_metrics(coco_eval)
+        eval_file = osp.join(output_dir, 'detection_results.pkl')
+        with open(eval_file, 'wb') as fid:
+            cPickle.dump(coco_eval, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'Wrote COCO eval results to: {}'.format(eval_file)
+
+    def _coco_results_one_category(self, boxes, cat_id):
+        results = []
+        for im_ind, index in enumerate(self.image_index):
+            dets = boxes[im_ind].astype(np.float)
+            if dets == []:
+                continue
+            scores = dets[:, -1]
+            xs = dets[:, 0]
+            ys = dets[:, 1]
+            ws = dets[:, 2] - xs + 1
+            hs = dets[:, 3] - ys + 1
+            results.extend(
+              [{'image_id' : index,
+                'category_id' : cat_id,
+                'bbox' : [xs[k], ys[k], ws[k], hs[k]],
+                'score' : scores[k]} for k in xrange(dets.shape[0])])
+        return results
+
+    def _write_coco_results_file(self, all_boxes, res_file):
+        # [{"image_id": 42,
+        #   "category_id": 18,
+        #   "bbox": [258.15,41.29,348.26,243.78],
+        #   "score": 0.236}, ...]
+        results = []
+        for cls_ind, cls in enumerate(self.classes):
+            if cls == '__background__':
+                continue
+            print 'Collecting {} results ({:d}/{:d})'.format(cls, cls_ind,
+                                                          self.num_classes - 1)
+            coco_cat_id = self._class_to_coco_cat_id[cls]
+            results.extend(self._coco_results_one_category(all_boxes[cls_ind],
+                                                           coco_cat_id))
+        print 'Writing results json to {}'.format(res_file)
+        with open(res_file, 'w') as fid:
+            json.dump(results, fid)
+
+    def evaluate_detections(self, all_boxes, output_dir):
+        res_file = osp.join(output_dir, ('detections_' +
+                                         self._image_set +
+                                         self._year +
+                                         '_results'))
+        if self.config['use_salt']:
+            res_file += '_{}'.format(str(uuid.uuid4()))
+        res_file += '.json'
+        self._write_coco_results_file(all_boxes, res_file)
+        # Only do evaluation on non-test sets
+        if self._image_set.find('test') == -1:
+            self._do_detection_eval(res_file, output_dir)
+        # Optionally cleanup results json file
+        if self.config['cleanup']:
+            os.remove(res_file)
+
+    def competition_mode(self, on):
+        if on:
+            self.config['use_salt'] = False
+            self.config['cleanup'] = False
+        else:
+            self.config['use_salt'] = True
+            self.config['cleanup'] = True
diff --git a/examples/rfcn/lib/datasets/ds_utils.py b/examples/rfcn/lib/datasets/ds_utils.py
new file mode 100644
index 000000000..f66a7f687
--- /dev/null
+++ b/examples/rfcn/lib/datasets/ds_utils.py
@@ -0,0 +1,41 @@
+# --------------------------------------------------------
+# Fast/er R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def unique_boxes(boxes, scale=1.0):
+    """Return indices of unique boxes."""
+    v = np.array([1, 1e3, 1e6, 1e9])
+    hashes = np.round(boxes * scale).dot(v)
+    _, index = np.unique(hashes, return_index=True)
+    return np.sort(index)
+
+def xywh_to_xyxy(boxes):
+    """Convert [x y w h] box format to [x1 y1 x2 y2] format."""
+    return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
+
+def xyxy_to_xywh(boxes):
+    """Convert [x1 y1 x2 y2] box format to [x y w h] format."""
+    return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
+
+def validate_boxes(boxes, width=0, height=0):
+    """Check that a set of boxes are valid."""
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    assert (x1 >= 0).all()
+    assert (y1 >= 0).all()
+    assert (x2 >= x1).all()
+    assert (y2 >= y1).all()
+    assert (x2 < width).all()
+    assert (y2 < height).all()
+
+def filter_small_boxes(boxes, min_size):
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+    keep = np.where((w >= min_size) & (h > min_size))[0]
+    return keep
diff --git a/examples/rfcn/lib/datasets/factory.py b/examples/rfcn/lib/datasets/factory.py
new file mode 100644
index 000000000..65de28d83
--- /dev/null
+++ b/examples/rfcn/lib/datasets/factory.py
@@ -0,0 +1,48 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Factory method for easily getting imdbs by name."""
+
+__sets = {}
+
+from datasets.pascal_voc import pascal_voc
+from datasets.coco import coco
+import numpy as np
+
+# Set up voc_<year>_<split> using selective search "fast" mode
+for year in ['2007', '2012', '0712']:
+    for split in ['train', 'val', 'trainval', 'test']:
+        name = 'voc_{}_{}'.format(year, split)
+        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
+
+# Set up coco_2014_<split>
+for year in ['2014']:
+    for split in ['train', 'val', 'minival', 'valminusminival']:
+        name = 'coco_{}_{}'.format(year, split)
+        __sets[name] = (lambda split=split, year=year: coco(split, year))
+
+# Set up coco_2015_<split>
+for year in ['2015']:
+    for split in ['test', 'test-dev']:
+        name = 'coco_{}_{}'.format(year, split)
+        __sets[name] = (lambda split=split, year=year: coco(split, year))
+
+# Set up luna_2016_<split>
+for year in ['2016']:
+    for split in ['trainval', 'test']:
+        name = 'luna_{}_{}'.format(year, split)
+        __sets[name] = (lambda split=split, year=year: luna(split, year))
+
+def get_imdb(name):
+    """Get an imdb (image database) by name."""
+    if not __sets.has_key(name):
+        raise KeyError('Unknown dataset: {}'.format(name))
+    return __sets[name]()
+
+def list_imdbs():
+    """List all registered imdbs."""
+    return __sets.keys()
diff --git a/examples/rfcn/lib/datasets/imdb.py b/examples/rfcn/lib/datasets/imdb.py
new file mode 100644
index 000000000..41c9ead06
--- /dev/null
+++ b/examples/rfcn/lib/datasets/imdb.py
@@ -0,0 +1,253 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import os
+import os.path as osp
+import PIL
+from utils.cython_bbox import bbox_overlaps
+import numpy as np
+import scipy.sparse
+from fast_rcnn.config import cfg
+
+class imdb(object):
+    """Image database."""
+
+    def __init__(self, name):
+        self._name = name
+        self._num_classes = 0
+        self._classes = []
+        self._image_index = []
+        self._obj_proposer = 'selective_search'
+        self._roidb = None
+        self._roidb_handler = self.default_roidb
+        # Use this dict for storing dataset specific config options
+        self.config = {}
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def num_classes(self):
+        return len(self._classes)
+
+    @property
+    def classes(self):
+        return self._classes
+
+    @property
+    def image_index(self):
+        return self._image_index
+
+    @property
+    def roidb_handler(self):
+        return self._roidb_handler
+
+    @roidb_handler.setter
+    def roidb_handler(self, val):
+        self._roidb_handler = val
+
+    def set_proposal_method(self, method):
+        method = eval('self.' + method + '_roidb')
+        self.roidb_handler = method
+
+    @property
+    def roidb(self):
+        # A roidb is a list of dictionaries, each with the following keys:
+        #   boxes
+        #   gt_overlaps
+        #   gt_classes
+        #   flipped
+        if self._roidb is not None:
+            return self._roidb
+        self._roidb = self.roidb_handler()
+        return self._roidb
+
+    @property
+    def cache_path(self):
+        cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
+        if not os.path.exists(cache_path):
+            os.makedirs(cache_path)
+        return cache_path
+
+    @property
+    def num_images(self):
+      return len(self.image_index)
+
+    def image_path_at(self, i):
+        raise NotImplementedError
+
+    def default_roidb(self):
+        raise NotImplementedError
+
+    def evaluate_detections(self, all_boxes, output_dir=None):
+        """
+        all_boxes is a list of length number-of-classes.
+        Each list element is a list of length number-of-images.
+        Each of those list elements is either an empty list []
+        or a numpy array of detection.
+
+        all_boxes[class][image] = [] or np.array of shape #dets x 5
+        """
+        raise NotImplementedError
+
+    def _get_widths(self):
+      return [PIL.Image.open(self.image_path_at(i)).size[0]
+              for i in xrange(self.num_images)]
+
+    def append_flipped_images(self):
+        num_images = self.num_images
+        widths = self._get_widths()
+        for i in xrange(num_images):
+            boxes = self.roidb[i]['boxes'].copy()
+            oldx1 = boxes[:, 0].copy()
+            oldx2 = boxes[:, 2].copy()
+            boxes[:, 0] = widths[i] - oldx2
+            boxes[:, 2] = widths[i] - oldx1
+            assert (boxes[:, 2] >= boxes[:, 0]).all()
+            entry = {'boxes' : boxes,
+                     'gt_overlaps' : self.roidb[i]['gt_overlaps'],
+                     'gt_classes' : self.roidb[i]['gt_classes'],
+                     'flipped' : True}
+            self.roidb.append(entry)
+        self._image_index = self._image_index * 2
+
+    def evaluate_recall(self, candidate_boxes=None, thresholds=None,
+                        area='all', limit=None):
+        """Evaluate detection proposal recall metrics.
+
+        Returns:
+            results: dictionary of results with keys
+                'ar': average recall
+                'recalls': vector recalls at each IoU overlap threshold
+                'thresholds': vector of IoU overlap thresholds
+                'gt_overlaps': vector of all ground-truth overlaps
+        """
+        # Record max overlap value for each gt box
+        # Return vector of overlap values
+        areas = { 'all': 0, 'small': 1, 'medium': 2, 'large': 3,
+                  '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
+        area_ranges = [ [0**2, 1e5**2],    # all
+                        [0**2, 32**2],     # small
+                        [32**2, 96**2],    # medium
+                        [96**2, 1e5**2],   # large
+                        [96**2, 128**2],   # 96-128
+                        [128**2, 256**2],  # 128-256
+                        [256**2, 512**2],  # 256-512
+                        [512**2, 1e5**2],  # 512-inf
+                      ]
+        assert areas.has_key(area), 'unknown area range: {}'.format(area)
+        area_range = area_ranges[areas[area]]
+        gt_overlaps = np.zeros(0)
+        num_pos = 0
+        for i in xrange(self.num_images):
+            # Checking for max_overlaps == 1 avoids including crowd annotations
+            # (...pretty hacking :/)
+            max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
+            gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
+                               (max_gt_overlaps == 1))[0]
+            gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
+            gt_areas = self.roidb[i]['seg_areas'][gt_inds]
+            valid_gt_inds = np.where((gt_areas >= area_range[0]) &
+                                     (gt_areas <= area_range[1]))[0]
+            gt_boxes = gt_boxes[valid_gt_inds, :]
+            num_pos += len(valid_gt_inds)
+
+            if candidate_boxes is None:
+                # If candidate_boxes is not supplied, the default is to use the
+                # non-ground-truth boxes from this roidb
+                non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
+                boxes = self.roidb[i]['boxes'][non_gt_inds, :]
+            else:
+                boxes = candidate_boxes[i]
+            if boxes.shape[0] == 0:
+                continue
+            if limit is not None and boxes.shape[0] > limit:
+                boxes = boxes[:limit, :]
+
+            overlaps = bbox_overlaps(boxes.astype(np.float),
+                                     gt_boxes.astype(np.float))
+
+            _gt_overlaps = np.zeros((gt_boxes.shape[0]))
+            for j in xrange(gt_boxes.shape[0]):
+                # find which proposal box maximally covers each gt box
+                argmax_overlaps = overlaps.argmax(axis=0)
+                # and get the iou amount of coverage for each gt box
+                max_overlaps = overlaps.max(axis=0)
+                # find which gt box is 'best' covered (i.e. 'best' = most iou)
+                gt_ind = max_overlaps.argmax()
+                gt_ovr = max_overlaps.max()
+                assert(gt_ovr >= 0)
+                # find the proposal box that covers the best covered gt box
+                box_ind = argmax_overlaps[gt_ind]
+                # record the iou coverage of this gt box
+                _gt_overlaps[j] = overlaps[box_ind, gt_ind]
+                assert(_gt_overlaps[j] == gt_ovr)
+                # mark the proposal box and the gt box as used
+                overlaps[box_ind, :] = -1
+                overlaps[:, gt_ind] = -1
+            # append recorded iou coverage level
+            gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
+
+        gt_overlaps = np.sort(gt_overlaps)
+        if thresholds is None:
+            step = 0.05
+            thresholds = np.arange(0.5, 0.95 + 1e-5, step)
+        recalls = np.zeros_like(thresholds)
+        # compute recall for each iou threshold
+        for i, t in enumerate(thresholds):
+            recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
+        # ar = 2 * np.trapz(recalls, thresholds)
+        ar = recalls.mean()
+        return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
+                'gt_overlaps': gt_overlaps}
+
+    def create_roidb_from_box_list(self, box_list, gt_roidb):
+        assert len(box_list) == self.num_images, \
+                'Number of boxes must match number of ground-truth images'
+        roidb = []
+        for i in xrange(self.num_images):
+            boxes = box_list[i]
+            num_boxes = boxes.shape[0]
+            overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
+
+            if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
+                gt_boxes = gt_roidb[i]['boxes']
+                gt_classes = gt_roidb[i]['gt_classes']
+                gt_overlaps = bbox_overlaps(boxes.astype(np.float),
+                                            gt_boxes.astype(np.float))
+                argmaxes = gt_overlaps.argmax(axis=1)
+                maxes = gt_overlaps.max(axis=1)
+                I = np.where(maxes > 0)[0]
+                overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
+
+            overlaps = scipy.sparse.csr_matrix(overlaps)
+            roidb.append({
+                'boxes' : boxes,
+                'gt_classes' : np.zeros((num_boxes,), dtype=np.int32),
+                'gt_overlaps' : overlaps,
+                'flipped' : False,
+                'seg_areas' : np.zeros((num_boxes,), dtype=np.float32),
+            })
+        return roidb
+
+    @staticmethod
+    def merge_roidbs(a, b):
+        assert len(a) == len(b)
+        for i in xrange(len(a)):
+            a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
+            a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
+                                            b[i]['gt_classes']))
+            a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
+                                                       b[i]['gt_overlaps']])
+            a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
+                                           b[i]['seg_areas']))
+        return a
+
+    def competition_mode(self, on):
+        """Turn competition mode on or off."""
+        pass
diff --git a/examples/rfcn/lib/datasets/luna.py b/examples/rfcn/lib/datasets/luna.py
new file mode 100644
index 000000000..55828ea6d
--- /dev/null
+++ b/examples/rfcn/lib/datasets/luna.py
@@ -0,0 +1,169 @@
+# --------------------------------------------------------
+# LUNA dataset reader
+# YAO Matrix (yaoweifeng0301@126.com)
+# --------------------------------------------------------
+
+import os
+from datasets.imdb import imdb
+import datasets.ds_utils as ds_utils
+import xml.etree.ElementTree as ET
+import numpy as np
+import scipy.sparse
+import scipy.io as sio
+import utils.cython_bbox
+import cPickle
+import subprocess
+import uuid
+import gzip
+from fast_rcnn.config import cfg
+
+class luna(imdb):
+    def __init__(self, image_set, year, data_path=None):
+        imdb.__init__(self, 'luna_' + year + '_' + image_set)
+        self._year = year
+        self._image_set = image_set
+        self._data_path = self._get_default_path() if data_path is None \
+                            else data_path
+        self._classes = ('__background__', # always index 0
+                         'nodule')
+        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
+        self._image_ext = '.pickle.gz'
+        self._image_index = self._load_image_set_index()
+        # Default to roidb handler
+        self._roidb_handler = self.gt_roidb
+        self._salt = str(uuid.uuid4())
+        self._comp_id = 'comp4'
+
+        # LUNA specific config options
+        self.config = {'cleanup'     : True,
+                       'use_salt'    : True,
+                       'rpn_file'    : None,
+                       'min_size'    : 2}
+
+        assert os.path.exists(self._data_path), \
+                'LUNA data path does not exist: {}'.format(self._data_path)
+
+    def image_path_at(self, i):
+        """
+        Return the absolute path to image i in the image sequence.
+        """
+        return self.image_path_from_index(self._image_index[i])
+
+    def image_path_from_index(self, index):
+        """
+        Construct an image path from the image's "index" identifier.
+        """
+        bb_directory = os.path.join(self._data_path, '1_1_1mm_slices_lung')
+        [subfolder, filename] = index.split('-')
+        image_path = os.path.join(bb_directory, 'subset' + str(subfolder), filename)
+        assert os.path.exists(image_path), \
+                'Path does not exist: {}'.format(image_path)
+        return image_path
+
+    def _load_image_set_index(self):
+        """
+        Load the indexes listed in this dataset's image set file.
+        """
+        # Example path to image set file:
+        image_set_file = os.path.join(self._data_path, 'ImageSets',
+                                      self._image_set + '.txt')
+        assert os.path.exists(image_set_file), \
+                'Path does not exist: {}'.format(image_set_file)
+        with open(image_set_file) as f:
+            image_index = [x.strip() for x in f.readlines()]
+        return image_index
+
+    def _get_default_path(self):
+        """
+        Return the default path where LUNA is expected to be installed.
+        """
+        return os.path.join(cfg.DATA_DIR, 'LUNA' + self._year)
+
+    def gt_roidb(self):
+        """
+        Return the database of ground-truth regions of interest.
+
+        This function loads/saves from/to a cache file to speed up future calls.
+        """
+        cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
+        if os.path.exists(cache_file):
+            with open(cache_file, 'rb') as fid:
+                roidb = cPickle.load(fid)
+            print '{} gt roidb loaded from {}'.format(self.name, cache_file)
+            return roidb
+
+        gt_roidb = [self._load_luna_annotation(index)
+                    for index in self.image_index]
+        with open(cache_file, 'wb') as fid:
+            cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'wrote gt roidb to {}'.format(cache_file)
+
+        return gt_roidb
+
+    def rpn_roidb(self):
+        if self._image_set != 'test':
+            gt_roidb = self.gt_roidb()
+            rpn_roidb = self._load_rpn_roidb(gt_roidb)
+            roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
+        else:
+            roidb = self._load_rpn_roidb(None)
+
+        return roidb
+
+    def _load_rpn_roidb(self, gt_roidb):
+        filename = self.config['rpn_file']
+        print 'loading {}'.format(filename)
+        assert os.path.exists(filename), \
+               'rpn data not found at: {}'.format(filename)
+        with open(filename, 'rb') as f:
+            box_list = cPickle.load(f)
+        return self.create_roidb_from_box_list(box_list, gt_roidb)
+
+    def _load_luna_annotation(self, index):
+        """
+        Load image and bounding boxes info from luna bbox file format
+        """
+        bb_directory = os.path.join(self._data_path, '1_1_1mm_slices_bbox')
+        [subfolder, filename] = index.split('-')
+        abs_filename = os.path.join(bb_directory, 'subset' + str(subfolder), filename)
+
+        with gzip.open(abs_filename, 'rb') as f:
+            [gt_boxes, im_info] = cPickle.load(f)
+            num_objs = len(gt_boxes)
+
+            boxes = np.zeros((num_objs, 4), dtype = np.uint16)
+            gt_classes = np.zeros((num_objs), dtype = np.int32)
+            overlaps = np.zeros((num_objs, self.num_classes), dtype = np.float32)
+            # "Seg" area for luna is just the box area
+            seg_areas = np.zeros((num_objs), dtype = np.float32)
+
+            # Load object bounding boxes into a data frame.
+            for ix, obj in enumerate(gt_boxes):
+                # Make pixel indexes 0-based
+                x1 = obj[0]
+                y1 = obj[1]
+                x2 = obj[2]
+                y2 = obj[3]
+                cls = int(obj[4])
+                boxes[ix, :] = [x1, y1, x2, y2]
+                gt_classes[ix] = cls
+                overlaps[ix, cls] = 1.0
+                seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+            overlaps = scipy.sparse.csr_matrix(overlaps)
+
+            return {'boxes' : boxes,
+                    'gt_classes': gt_classes,
+                    'gt_overlaps' : overlaps,
+                    'flipped' : False,
+                    'seg_areas' : seg_areas}
+
+    def _get_comp_id(self):
+        comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] else self._comp_id)
+        return comp_id
+    
+if __name__ == '__main__':
+    from datasets.luna import luna
+    d = luna('trainval', '2016')
+    res = d.roidb
+    from IPython import embed; embed()
diff --git a/examples/rfcn/lib/datasets/pascal_voc.py b/examples/rfcn/lib/datasets/pascal_voc.py
new file mode 100644
index 000000000..b55f2f6b2
--- /dev/null
+++ b/examples/rfcn/lib/datasets/pascal_voc.py
@@ -0,0 +1,344 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import os
+from datasets.imdb import imdb
+import datasets.ds_utils as ds_utils
+import xml.etree.ElementTree as ET
+import numpy as np
+import scipy.sparse
+import scipy.io as sio
+import utils.cython_bbox
+import cPickle
+import subprocess
+import uuid
+from voc_eval import voc_eval
+from fast_rcnn.config import cfg
+
+class pascal_voc(imdb):
+    def __init__(self, image_set, year, devkit_path=None):
+        imdb.__init__(self, 'voc_' + year + '_' + image_set)
+        self._year = year
+        self._image_set = image_set
+        self._devkit_path = self._get_default_path() if devkit_path is None \
+                            else devkit_path
+        self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
+        self._classes = ('__background__', # always index 0
+                         'aeroplane', 'bicycle', 'bird', 'boat',
+                         'bottle', 'bus', 'car', 'cat', 'chair',
+                         'cow', 'diningtable', 'dog', 'horse',
+                         'motorbike', 'person', 'pottedplant',
+                         'sheep', 'sofa', 'train', 'tvmonitor')
+        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
+        self._image_ext = '.jpg'
+        self._image_index = self._load_image_set_index()
+        # Default to roidb handler
+        self._roidb_handler = self.selective_search_roidb
+        self._salt = str(uuid.uuid4())
+        self._comp_id = 'comp4'
+
+        # PASCAL specific config options
+        self.config = {'cleanup'     : True,
+                       'use_salt'    : True,
+                       'use_diff'    : False,
+                       'matlab_eval' : False,
+                       'rpn_file'    : None,
+                       'min_size'    : 2}
+
+        assert os.path.exists(self._devkit_path), \
+                'VOCdevkit path does not exist: {}'.format(self._devkit_path)
+        assert os.path.exists(self._data_path), \
+                'Path does not exist: {}'.format(self._data_path)
+
+    def image_path_at(self, i):
+        """
+        Return the absolute path to image i in the image sequence.
+        """
+        return self.image_path_from_index(self._image_index[i])
+
+    def image_path_from_index(self, index):
+        """
+        Construct an image path from the image's "index" identifier.
+        """
+        image_path = os.path.join(self._data_path, 'JPEGImages',
+                                  index + self._image_ext)
+        assert os.path.exists(image_path), \
+                'Path does not exist: {}'.format(image_path)
+        return image_path
+
+    def _load_image_set_index(self):
+        """
+        Load the indexes listed in this dataset's image set file.
+        """
+        # Example path to image set file:
+        # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
+        image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
+                                      self._image_set + '.txt')
+        assert os.path.exists(image_set_file), \
+                'Path does not exist: {}'.format(image_set_file)
+        with open(image_set_file) as f:
+            image_index = [x.strip() for x in f.readlines()]
+        return image_index
+
+    def _get_default_path(self):
+        """
+        Return the default path where PASCAL VOC is expected to be installed.
+        """
+        return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
+
+    def gt_roidb(self):
+        """
+        Return the database of ground-truth regions of interest.
+
+        This function loads/saves from/to a cache file to speed up future calls.
+        """
+        cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
+        if os.path.exists(cache_file):
+            with open(cache_file, 'rb') as fid:
+                roidb = cPickle.load(fid)
+            print '{} gt roidb loaded from {}'.format(self.name, cache_file)
+            return roidb
+
+        gt_roidb = [self._load_pascal_annotation(index)
+                    for index in self.image_index]
+        with open(cache_file, 'wb') as fid:
+            cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'wrote gt roidb to {}'.format(cache_file)
+
+        return gt_roidb
+
+    def selective_search_roidb(self):
+        """
+        Return the database of selective search regions of interest.
+        Ground-truth ROIs are also included.
+
+        This function loads/saves from/to a cache file to speed up future calls.
+        """
+        cache_file = os.path.join(self.cache_path,
+                                  self.name + '_selective_search_roidb.pkl')
+
+        if os.path.exists(cache_file):
+            with open(cache_file, 'rb') as fid:
+                roidb = cPickle.load(fid)
+            print '{} ss roidb loaded from {}'.format(self.name, cache_file)
+            return roidb
+
+        if int(self._year) == 2007 or self._image_set != 'test':
+            gt_roidb = self.gt_roidb()
+            ss_roidb = self._load_selective_search_roidb(gt_roidb)
+            roidb = imdb.merge_roidbs(gt_roidb, ss_roidb)
+        else:
+            roidb = self._load_selective_search_roidb(None)
+        with open(cache_file, 'wb') as fid:
+            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
+        print 'wrote ss roidb to {}'.format(cache_file)
+
+        return roidb
+
+    def rpn_roidb(self):
+        if int(self._year) == 2007 or self._image_set != 'test':
+            gt_roidb = self.gt_roidb()
+            rpn_roidb = self._load_rpn_roidb(gt_roidb)
+            roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
+        else:
+            roidb = self._load_rpn_roidb(None)
+
+        return roidb
+
+    def _load_rpn_roidb(self, gt_roidb):
+        filename = self.config['rpn_file']
+        print 'loading {}'.format(filename)
+        assert os.path.exists(filename), \
+               'rpn data not found at: {}'.format(filename)
+        with open(filename, 'rb') as f:
+            box_list = cPickle.load(f)
+        return self.create_roidb_from_box_list(box_list, gt_roidb)
+
+    def _load_selective_search_roidb(self, gt_roidb):
+        filename = os.path.abspath(os.path.join(cfg.DATA_DIR,
+                                                'selective_search_data',
+                                                self.name + '.mat'))
+        assert os.path.exists(filename), \
+               'Selective search data not found at: {}'.format(filename)
+        raw_data = sio.loadmat(filename)['boxes'].ravel()
+
+        box_list = []
+        for i in xrange(raw_data.shape[0]):
+            boxes = raw_data[i][:, (1, 0, 3, 2)] - 1
+            keep = ds_utils.unique_boxes(boxes)
+            boxes = boxes[keep, :]
+            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
+            boxes = boxes[keep, :]
+            box_list.append(boxes)
+
+        return self.create_roidb_from_box_list(box_list, gt_roidb)
+
+    def _load_pascal_annotation(self, index):
+        """
+        Load image and bounding boxes info from XML file in the PASCAL VOC
+        format.
+        """
+        filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
+        tree = ET.parse(filename)
+        objs = tree.findall('object')
+        if not self.config['use_diff']:
+            # Exclude the samples labeled as difficult
+            non_diff_objs = [
+                obj for obj in objs if int(obj.find('difficult').text) == 0]
+            # if len(non_diff_objs) != len(objs):
+            #     print 'Removed {} difficult objects'.format(
+            #         len(objs) - len(non_diff_objs))
+            objs = non_diff_objs
+        num_objs = len(objs)
+
+        boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+        gt_classes = np.zeros((num_objs), dtype=np.int32)
+        overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
+        # "Seg" area for pascal is just the box area
+        seg_areas = np.zeros((num_objs), dtype=np.float32)
+
+        # Load object bounding boxes into a data frame.
+        for ix, obj in enumerate(objs):
+            bbox = obj.find('bndbox')
+            # Make pixel indexes 0-based
+            x1 = float(bbox.find('xmin').text) - 1
+            y1 = float(bbox.find('ymin').text) - 1
+            x2 = float(bbox.find('xmax').text) - 1
+            y2 = float(bbox.find('ymax').text) - 1
+            cls = self._class_to_ind[obj.find('name').text.lower().strip()]
+            boxes[ix, :] = [x1, y1, x2, y2]
+            gt_classes[ix] = cls
+            overlaps[ix, cls] = 1.0
+            seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+        overlaps = scipy.sparse.csr_matrix(overlaps)
+
+        return {'boxes' : boxes,
+                'gt_classes': gt_classes,
+                'gt_overlaps' : overlaps,
+                'flipped' : False,
+                'seg_areas' : seg_areas}
+
+    def _get_comp_id(self):
+        comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
+            else self._comp_id)
+        return comp_id
+
+    def _get_voc_results_file_template(self):
+        # VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
+        filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
+        path = os.path.join(
+            self._devkit_path,
+            'results',
+            'VOC' + self._year,
+            'Main',
+            filename)
+        return path
+
+    def _write_voc_results_file(self, all_boxes):
+        for cls_ind, cls in enumerate(self.classes):
+            if cls == '__background__':
+                continue
+            print 'Writing {} VOC results file'.format(cls)
+            filename = self._get_voc_results_file_template().format(cls)
+            with open(filename, 'wt') as f:
+                for im_ind, index in enumerate(self.image_index):
+                    dets = all_boxes[cls_ind][im_ind]
+                    if dets == []:
+                        continue
+                    # the VOCdevkit expects 1-based indices
+                    for k in xrange(dets.shape[0]):
+                        f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
+                                format(index, dets[k, -1],
+                                       dets[k, 0] + 1, dets[k, 1] + 1,
+                                       dets[k, 2] + 1, dets[k, 3] + 1))
+
+    def _do_python_eval(self, output_dir = 'output'):
+        annopath = os.path.join(
+            self._devkit_path,
+            'VOC' + self._year,
+            'Annotations',
+            '{:s}.xml')
+        imagesetfile = os.path.join(
+            self._devkit_path,
+            'VOC' + self._year,
+            'ImageSets',
+            'Main',
+            self._image_set + '.txt')
+        cachedir = os.path.join(self._devkit_path, 'annotations_cache')
+        aps = []
+        # The PASCAL VOC metric changed in 2010
+        use_07_metric = True if int(self._year) < 2010 else False
+        print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
+        if not os.path.isdir(output_dir):
+            os.mkdir(output_dir)
+        for i, cls in enumerate(self._classes):
+            if cls == '__background__':
+                continue
+            filename = self._get_voc_results_file_template().format(cls)
+            rec, prec, ap = voc_eval(
+                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
+                use_07_metric=use_07_metric)
+            aps += [ap]
+            print('AP for {} = {:.4f}'.format(cls, ap))
+            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:
+                cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
+        print('Mean AP = {:.4f}'.format(np.mean(aps)))
+        print('~~~~~~~~')
+        print('Results:')
+        for ap in aps:
+            print('{:.3f}'.format(ap))
+        print('{:.3f}'.format(np.mean(aps)))
+        print('~~~~~~~~')
+        print('')
+        print('--------------------------------------------------------------')
+        print('Results computed with the **unofficial** Python eval code.')
+        print('Results should be very close to the official MATLAB eval code.')
+        print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
+        print('-- Thanks, The Management')
+        print('--------------------------------------------------------------')
+
+    def _do_matlab_eval(self, output_dir='output'):
+        print '-----------------------------------------------------'
+        print 'Computing results with the official MATLAB eval code.'
+        print '-----------------------------------------------------'
+        path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
+                            'VOCdevkit-matlab-wrapper')
+        cmd = 'cd {} && '.format(path)
+        cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
+        cmd += '-r "dbstop if error; '
+        cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
+               .format(self._devkit_path, self._get_comp_id(),
+                       self._image_set, output_dir)
+        print('Running:\n{}'.format(cmd))
+        status = subprocess.call(cmd, shell=True)
+
+    def evaluate_detections(self, all_boxes, output_dir):
+        self._write_voc_results_file(all_boxes)
+        self._do_python_eval(output_dir)
+        if self.config['matlab_eval']:
+            self._do_matlab_eval(output_dir)
+        if self.config['cleanup']:
+            for cls in self._classes:
+                if cls == '__background__':
+                    continue
+                filename = self._get_voc_results_file_template().format(cls)
+                os.remove(filename)
+
+    def competition_mode(self, on):
+        if on:
+            self.config['use_salt'] = False
+            self.config['cleanup'] = False
+        else:
+            self.config['use_salt'] = True
+            self.config['cleanup'] = True
+
+if __name__ == '__main__':
+    from datasets.pascal_voc import pascal_voc
+    d = pascal_voc('trainval', '2007')
+    res = d.roidb
+    from IPython import embed; embed()
diff --git a/examples/rfcn/lib/datasets/tools/mcg_munge.py b/examples/rfcn/lib/datasets/tools/mcg_munge.py
new file mode 100644
index 000000000..1392aa308
--- /dev/null
+++ b/examples/rfcn/lib/datasets/tools/mcg_munge.py
@@ -0,0 +1,38 @@
+import os
+import sys
+
+"""Hacky tool to convert file system layout of MCG boxes downloaded from
+http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/
+so that it's consistent with those computed by Jan Hosang (see:
+http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
+  computing/research/object-recognition-and-scene-understanding/how-
+  good-are-detection-proposals-really/)
+
+NB: Boxes from the MCG website are in (y1, x1, y2, x2) order.
+Boxes from Hosang et al. are in (x1, y1, x2, y2) order.
+"""
+
+def munge(src_dir):
+    # stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat
+    # want:      ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat
+
+    files = os.listdir(src_dir)
+    for fn in files:
+        base, ext = os.path.splitext(fn)
+        # first 14 chars / first 22 chars / all chars + .mat
+        # COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
+        first = base[:14]
+        second = base[:22]
+        dst_dir = os.path.join('MCG', 'mat', first, second)
+        if not os.path.exists(dst_dir):
+            os.makedirs(dst_dir)
+        src = os.path.join(src_dir, fn)
+        dst = os.path.join(dst_dir, fn)
+        print 'MV: {} -> {}'.format(src, dst)
+        os.rename(src, dst)
+
+if __name__ == '__main__':
+    # src_dir should look something like:
+    #  src_dir = 'MCG-COCO-val2014-boxes'
+    src_dir = sys.argv[1]
+    munge(src_dir)
diff --git a/examples/rfcn/lib/datasets/voc_eval.py b/examples/rfcn/lib/datasets/voc_eval.py
new file mode 100644
index 000000000..8d0a83076
--- /dev/null
+++ b/examples/rfcn/lib/datasets/voc_eval.py
@@ -0,0 +1,200 @@
+# --------------------------------------------------------
+# Fast/er R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Bharath Hariharan
+# --------------------------------------------------------
+
+import xml.etree.ElementTree as ET
+import os
+import cPickle
+import numpy as np
+
+def parse_rec(filename):
+    """ Parse a PASCAL VOC xml file """
+    tree = ET.parse(filename)
+    objects = []
+    for obj in tree.findall('object'):
+        obj_struct = {}
+        obj_struct['name'] = obj.find('name').text
+        obj_struct['pose'] = obj.find('pose').text
+        obj_struct['truncated'] = int(obj.find('truncated').text)
+        obj_struct['difficult'] = int(obj.find('difficult').text)
+        bbox = obj.find('bndbox')
+        obj_struct['bbox'] = [int(bbox.find('xmin').text),
+                              int(bbox.find('ymin').text),
+                              int(bbox.find('xmax').text),
+                              int(bbox.find('ymax').text)]
+        objects.append(obj_struct)
+
+    return objects
+
+def voc_ap(rec, prec, use_07_metric=False):
+    """ ap = voc_ap(rec, prec, [use_07_metric])
+    Compute VOC AP given precision and recall.
+    If use_07_metric is true, uses the
+    VOC 07 11 point method (default:False).
+    """
+    if use_07_metric:
+        # 11 point metric
+        ap = 0.
+        for t in np.arange(0., 1.1, 0.1):
+            if np.sum(rec >= t) == 0:
+                p = 0
+            else:
+                p = np.max(prec[rec >= t])
+            ap = ap + p / 11.
+    else:
+        # correct AP calculation
+        # first append sentinel values at the end
+        mrec = np.concatenate(([0.], rec, [1.]))
+        mpre = np.concatenate(([0.], prec, [0.]))
+
+        # compute the precision envelope
+        for i in range(mpre.size - 1, 0, -1):
+            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+        # to calculate area under PR curve, look for points
+        # where X axis (recall) changes value
+        i = np.where(mrec[1:] != mrec[:-1])[0]
+
+        # and sum (\Delta recall) * prec
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+    return ap
+
+def voc_eval(detpath,
+             annopath,
+             imagesetfile,
+             classname,
+             cachedir,
+             ovthresh=0.5,
+             use_07_metric=False):
+    """rec, prec, ap = voc_eval(detpath,
+                                annopath,
+                                imagesetfile,
+                                classname,
+                                [ovthresh],
+                                [use_07_metric])
+
+    Top level function that does the PASCAL VOC evaluation.
+
+    detpath: Path to detections
+        detpath.format(classname) should produce the detection results file.
+    annopath: Path to annotations
+        annopath.format(imagename) should be the xml annotations file.
+    imagesetfile: Text file containing the list of images, one image per line.
+    classname: Category name (duh)
+    cachedir: Directory for caching the annotations
+    [ovthresh]: Overlap threshold (default = 0.5)
+    [use_07_metric]: Whether to use VOC07's 11 point AP computation
+        (default False)
+    """
+    # assumes detections are in detpath.format(classname)
+    # assumes annotations are in annopath.format(imagename)
+    # assumes imagesetfile is a text file with each line an image name
+    # cachedir caches the annotations in a pickle file
+
+    # first load gt
+    if not os.path.isdir(cachedir):
+        os.mkdir(cachedir)
+    cachefile = os.path.join(cachedir, 'annots.pkl')
+    # read list of images
+    with open(imagesetfile, 'r') as f:
+        lines = f.readlines()
+    imagenames = [x.strip() for x in lines]
+
+    if not os.path.isfile(cachefile):
+        # load annots
+        recs = {}
+        for i, imagename in enumerate(imagenames):
+            recs[imagename] = parse_rec(annopath.format(imagename))
+            if i % 100 == 0:
+                print 'Reading annotation for {:d}/{:d}'.format(
+                    i + 1, len(imagenames))
+        # save
+        print 'Saving cached annotations to {:s}'.format(cachefile)
+        with open(cachefile, 'w') as f:
+            cPickle.dump(recs, f)
+    else:
+        # load
+        with open(cachefile, 'r') as f:
+            recs = cPickle.load(f)
+
+    # extract gt objects for this class
+    class_recs = {}
+    npos = 0
+    for imagename in imagenames:
+        R = [obj for obj in recs[imagename] if obj['name'] == classname]
+        bbox = np.array([x['bbox'] for x in R])
+        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
+        det = [False] * len(R)
+        npos = npos + sum(~difficult)
+        class_recs[imagename] = {'bbox': bbox,
+                                 'difficult': difficult,
+                                 'det': det}
+
+    # read dets
+    detfile = detpath.format(classname)
+    with open(detfile, 'r') as f:
+        lines = f.readlines()
+
+    splitlines = [x.strip().split(' ') for x in lines]
+    image_ids = [x[0] for x in splitlines]
+    confidence = np.array([float(x[1]) for x in splitlines])
+    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
+
+    # sort by confidence
+    sorted_ind = np.argsort(-confidence)
+    sorted_scores = np.sort(-confidence)
+    BB = BB[sorted_ind, :]
+    image_ids = [image_ids[x] for x in sorted_ind]
+
+    # go down dets and mark TPs and FPs
+    nd = len(image_ids)
+    tp = np.zeros(nd)
+    fp = np.zeros(nd)
+    for d in range(nd):
+        R = class_recs[image_ids[d]]
+        bb = BB[d, :].astype(float)
+        ovmax = -np.inf
+        BBGT = R['bbox'].astype(float)
+
+        if BBGT.size > 0:
+            # compute overlaps
+            # intersection
+            ixmin = np.maximum(BBGT[:, 0], bb[0])
+            iymin = np.maximum(BBGT[:, 1], bb[1])
+            ixmax = np.minimum(BBGT[:, 2], bb[2])
+            iymax = np.minimum(BBGT[:, 3], bb[3])
+            iw = np.maximum(ixmax - ixmin + 1., 0.)
+            ih = np.maximum(iymax - iymin + 1., 0.)
+            inters = iw * ih
+
+            # union
+            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
+                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
+                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
+
+            overlaps = inters / uni
+            ovmax = np.max(overlaps)
+            jmax = np.argmax(overlaps)
+
+        if ovmax > ovthresh:
+            if not R['difficult'][jmax]:
+                if not R['det'][jmax]:
+                    tp[d] = 1.
+                    R['det'][jmax] = 1
+                else:
+                    fp[d] = 1.
+        else:
+            fp[d] = 1.
+
+    # compute precision recall
+    fp = np.cumsum(fp)
+    tp = np.cumsum(tp)
+    rec = tp / float(npos)
+    # avoid divide by zero in case the first detection matches a difficult
+    # ground truth
+    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
+    ap = voc_ap(rec, prec, use_07_metric)
+
+    return rec, prec, ap
diff --git a/examples/rfcn/lib/fast_rcnn/__init__.py b/examples/rfcn/lib/fast_rcnn/__init__.py
new file mode 100644
index 000000000..7ba6a65c5
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/__init__.py
@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
diff --git a/examples/rfcn/lib/fast_rcnn/bbox_transform.py b/examples/rfcn/lib/fast_rcnn/bbox_transform.py
new file mode 100644
index 000000000..c02916305
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/bbox_transform.py
@@ -0,0 +1,76 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def bbox_transform(ex_rois, gt_rois):
+    ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
+    ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
+    ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
+    ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
+
+    gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
+    gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
+    gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
+    gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
+
+    targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
+    targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights
+    targets_dw = np.log(gt_widths / ex_widths)
+    targets_dh = np.log(gt_heights / ex_heights)
+
+    targets = np.vstack(
+        (targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
+    return targets
+
+def bbox_transform_inv(boxes, deltas):
+    if boxes.shape[0] == 0:
+        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
+
+    boxes = boxes.astype(deltas.dtype, copy=False)
+
+    widths = boxes[:, 2] - boxes[:, 0] + 1.0
+    heights = boxes[:, 3] - boxes[:, 1] + 1.0
+    ctr_x = boxes[:, 0] + 0.5 * widths
+    ctr_y = boxes[:, 1] + 0.5 * heights
+
+    dx = deltas[:, 0::4]
+    dy = deltas[:, 1::4]
+    dw = deltas[:, 2::4]
+    dh = deltas[:, 3::4]
+
+    pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
+    pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
+    pred_w = np.exp(dw) * widths[:, np.newaxis]
+    pred_h = np.exp(dh) * heights[:, np.newaxis]
+
+    pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
+    # x1
+    pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
+    # y1
+    pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
+    # x2
+    pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
+    # y2
+    pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
+
+    return pred_boxes
+
+def clip_boxes(boxes, im_shape):
+    """
+    Clip boxes to image boundaries.
+    """
+
+    # x1 >= 0
+    boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
+    # y1 >= 0
+    boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
+    # x2 < im_shape[1]
+    boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
+    # y2 < im_shape[0]
+    boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
+    return boxes
diff --git a/examples/rfcn/lib/fast_rcnn/config.py b/examples/rfcn/lib/fast_rcnn/config.py
new file mode 100644
index 000000000..e838ba3b9
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/config.py
@@ -0,0 +1,300 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Fast R-CNN config system.
+
+This file specifies default config options for Fast R-CNN. You should not
+change values in this file. Instead, you should write a config file (in yaml)
+and use cfg_from_file(yaml_file) to load it and override the default options.
+
+Most tools in $ROOT/tools take a --cfg option to specify an override file.
+    - See tools/{train,test}_net.py for example code that uses cfg_from_file()
+    - See experiments/cfgs/*.yml for example YAML config override files
+"""
+
+import os
+import os.path as osp
+import numpy as np
+# `pip install easydict` if you don't have it
+from easydict import EasyDict as edict
+
+__C = edict()
+# Consumers can get config by:
+#   from fast_rcnn_config import cfg
+cfg = __C
+
+#
+# Training options
+#
+
+__C.TRAIN = edict()
+
+# Scales to use during training (can list multiple scales)
+# Each scale is the pixel size of an image's shortest side
+__C.TRAIN.SCALES = (600,)
+
+# pickle or image
+__C.TRAIN.FORMAT = '' # 'pickle'
+
+# Max pixel size of the longest side of a scaled input image
+__C.TRAIN.MAX_SIZE = 800
+
+# Images to use per minibatch
+__C.TRAIN.IMS_PER_BATCH = 2
+
+# Minibatch size (number of regions of interest [ROIs])
+__C.TRAIN.BATCH_SIZE = 128
+
+# Fraction of minibatch that is labeled foreground (i.e. class > 0)
+__C.TRAIN.FG_FRACTION = 0.25
+
+# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
+__C.TRAIN.FG_THRESH = 0.5
+
+# Overlap threshold for a ROI to be considered background (class = 0 if
+# overlap in [LO, HI))
+__C.TRAIN.BG_THRESH_HI = 0.5
+__C.TRAIN.BG_THRESH_LO = 0.1
+
+# Use horizontally-flipped images during training?
+__C.TRAIN.USE_FLIPPED = True
+
+# Train bounding-box regressors
+__C.TRAIN.BBOX_REG = True
+
+# Overlap required between a ROI and ground-truth box in order for that ROI to
+# be used as a bounding-box regression training example
+__C.TRAIN.BBOX_THRESH = 0.5
+
+# Iterations between snapshots
+__C.TRAIN.SNAPSHOT_ITERS = 10000
+
+# solver.prototxt specifies the snapshot path prefix, this adds an optional
+# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
+__C.TRAIN.SNAPSHOT_INFIX = ''
+
+# Use a prefetch thread in roi_data_layer.layer
+# So far I haven't found this useful; likely more engineering work is required
+__C.TRAIN.USE_PREFETCH = False
+
+# Normalize the targets (subtract empirical mean, divide by empirical stddev)
+__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
+# Deprecated (inside weights)
+__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+# Normalize the targets using "precomputed" (or made up) means and stdevs
+# (BBOX_NORMALIZE_TARGETS must also be True)
+__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = False
+__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
+__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
+
+__C.TRAIN.RPN_NORMALIZE_TARGETS = False
+__C.TRAIN.RPN_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
+__C.TRAIN.RPN_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
+
+# Train using these proposals
+__C.TRAIN.PROPOSAL_METHOD = 'selective_search'
+
+# Make minibatches from images that have similar aspect ratios (i.e. both
+# tall and thin or both short and wide) in order to avoid wasting computation
+# on zero-padding.
+__C.TRAIN.ASPECT_GROUPING = True
+
+# Use RPN to detect objects
+__C.TRAIN.HAS_RPN = False
+# IOU >= thresh: positive example
+__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
+# IOU < thresh: negative example
+__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
+# If an anchor statisfied by positive and negative conditions set to negative
+__C.TRAIN.RPN_CLOBBER_POSITIVES = False
+# Max number of foreground examples
+__C.TRAIN.RPN_FG_FRACTION = 0.5
+# Total number of examples
+__C.TRAIN.RPN_BATCHSIZE = 256
+# NMS threshold used on RPN proposals
+__C.TRAIN.RPN_NMS_THRESH = 0.7
+# Number of top scoring boxes to keep before apply NMS to RPN proposals
+__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
+# Number of top scoring boxes to keep after applying NMS to RPN proposals
+__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
+# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
+__C.TRAIN.RPN_MIN_SIZE = 16
+# Deprecated (outside weights)
+__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+# Give the positive RPN examples weight of p * 1 / {num positives}
+# and give negatives a weight of (1 - p)
+# Set to -1.0 to use uniform example weighting
+__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
+
+# whether use class aware box or not
+__C.TRAIN.AGNOSTIC = False
+
+#
+# Testing options
+#
+
+__C.TEST = edict()
+
+# Scales to use during testing (can list multiple scales)
+# Each scale is the pixel size of an image's shortest side
+__C.TEST.SCALES = (600,)
+
+# pickle or image
+__C.TEST.FORMAT = '' # 'pickle'
+
+# Max pixel size of the longest side of a scaled input image
+__C.TEST.MAX_SIZE = 1000
+
+# Overlap threshold used for non-maximum suppression (suppress boxes with
+# IoU >= this threshold)
+__C.TEST.NMS = 0.3
+
+# Experimental: treat the (K+1) units in the cls_score layer as linear
+# predictors (trained, eg, with one-vs-rest SVMs).
+__C.TEST.SVM = False
+
+# Test using bounding-box regressors
+__C.TEST.BBOX_REG = True
+
+# Propose boxes
+__C.TEST.HAS_RPN = False
+
+# Test using these proposals
+__C.TEST.PROPOSAL_METHOD = 'selective_search'
+
+## NMS threshold used on RPN proposals
+__C.TEST.RPN_NMS_THRESH = 0.7
+## Number of top scoring boxes to keep before apply NMS to RPN proposals
+__C.TEST.RPN_PRE_NMS_TOP_N = 6000
+## Number of top scoring boxes to keep after applying NMS to RPN proposals
+__C.TEST.RPN_POST_NMS_TOP_N = 300
+# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
+__C.TEST.RPN_MIN_SIZE = 16
+
+# whether use class aware box or not
+__C.TEST.AGNOSTIC = False
+
+
+#
+# MISC
+#
+
+# The mapping from image coordinates to feature map coordinates might cause
+# some boxes that are distinct in image space to become identical in feature
+# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
+# for identifying duplicate boxes.
+# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
+__C.DEDUP_BOXES = 1./16.
+
+# Pixel mean values (BGR order) as a (1, 1, 3) array
+# We use the same pixel mean for all networks even though it's not exactly what
+# they were trained with
+__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
+
+# For reproducibility
+__C.RNG_SEED = 3
+
+# A small number that's used many times
+__C.EPS = 1e-14
+
+# Root directory of project
+__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
+
+# Data directory
+__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
+
+# Model directory
+__C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, '..', '..', 'models', 'intel_optimized_models', 'rfcn', 'pascal_voc'))
+
+# Name (or path to) the matlab executable
+__C.MATLAB = 'matlab'
+
+# Place outputs under an experiments directory
+__C.EXP_DIR = 'default'
+
+# Use GPU implementation of non-maximum suppression
+__C.USE_GPU_NMS = False
+
+# Default GPU device id
+__C.GPU_ID = 0
+
+
+def get_output_dir(imdb, net=None):
+    """Return the directory where experimental artifacts are placed.
+    If the directory does not exist, it is created.
+
+    A canonical path is built using the name from an imdb and a network
+    (if not None).
+    """
+    outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
+    if net is not None:
+        outdir = osp.join(outdir, net.name)
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+    return outdir
+
+def _merge_a_into_b(a, b):
+    """Merge config dictionary a into config dictionary b, clobbering the
+    options in b whenever they are also specified in a.
+    """
+    if type(a) is not edict:
+        return
+
+    for k, v in a.iteritems():
+        # a must specify keys that are in b
+        if not b.has_key(k):
+            raise KeyError('{} is not a valid config key'.format(k))
+
+        # the types must match, too
+        old_type = type(b[k])
+        if old_type is not type(v):
+            if isinstance(b[k], np.ndarray):
+                v = np.array(v, dtype=b[k].dtype)
+            else:
+                raise ValueError(('Type mismatch ({} vs. {}) '
+                                'for config key: {}').format(type(b[k]),
+                                                            type(v), k))
+
+        # recursively merge dicts
+        if type(v) is edict:
+            try:
+                _merge_a_into_b(a[k], b[k])
+            except:
+                print('Error under config key: {}'.format(k))
+                raise
+        else:
+            b[k] = v
+
+def cfg_from_file(filename):
+    """Load a config file and merge it into the default options."""
+    import yaml
+    with open(filename, 'r') as f:
+        yaml_cfg = edict(yaml.load(f))
+
+    _merge_a_into_b(yaml_cfg, __C)
+
+def cfg_from_list(cfg_list):
+    """Set config keys via list (e.g., from command line)."""
+    from ast import literal_eval
+    assert len(cfg_list) % 2 == 0
+    for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
+        key_list = k.split('.')
+        d = __C
+        for subkey in key_list[:-1]:
+            assert d.has_key(subkey)
+            d = d[subkey]
+        subkey = key_list[-1]
+        assert d.has_key(subkey)
+        try:
+            value = literal_eval(v)
+        except:
+            # handle the case when v is a string literal
+            value = v
+        assert type(value) == type(d[subkey]), \
+            'type {} does not match original type {}'.format(
+            type(value), type(d[subkey]))
+        d[subkey] = value
diff --git a/examples/rfcn/lib/fast_rcnn/nms_wrapper.py b/examples/rfcn/lib/fast_rcnn/nms_wrapper.py
new file mode 100644
index 000000000..a59485e69
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/nms_wrapper.py
@@ -0,0 +1,20 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+from fast_rcnn.config import cfg
+#from nms.gpu_nms import gpu_nms
+from nms.cpu_nms import cpu_nms
+
+def nms(dets, thresh, force_cpu=True):
+    """Dispatch to either CPU or GPU NMS implementations."""
+
+    if dets.shape[0] == 0:
+        return []
+    if cfg.USE_GPU_NMS and not force_cpu:
+        return gpu_nms(dets, thresh, device_id=cfg.GPU_ID)
+    else:
+        return cpu_nms(dets, thresh)
diff --git a/examples/rfcn/lib/fast_rcnn/test.py b/examples/rfcn/lib/fast_rcnn/test.py
new file mode 100644
index 000000000..d12c3a866
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/test.py
@@ -0,0 +1,305 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Test a Fast R-CNN network on an imdb (image database)."""
+
+from fast_rcnn.config import cfg, get_output_dir
+from fast_rcnn.bbox_transform import clip_boxes, bbox_transform_inv
+import argparse
+from utils.timer import Timer
+import numpy as np
+import cv2
+import caffe
+from fast_rcnn.nms_wrapper import nms
+import cPickle
+import gzip
+from utils.blob import im_list_to_blob
+import os
+
+def _get_image_blob(im):
+    """Converts an image into a network input.
+
+    Arguments:
+        im (ndarray): a color image in BGR order
+
+    Returns:
+        blob (ndarray): a data blob holding an image pyramid
+        im_scale_factors (list): list of image scales (relative to im) used
+            in the image pyramid
+    """
+    im_orig = im.astype(np.float32, copy=True)
+    im_orig -= cfg.PIXEL_MEANS
+
+    im_shape = im_orig.shape
+    im_size_min = np.min(im_shape[0:2])
+    im_size_max = np.max(im_shape[0:2])
+
+    processed_ims = []
+    im_scale_factors = []
+
+    for target_size in cfg.TEST.SCALES:
+        im_scale = float(target_size) / float(im_size_min)
+        # Prevent the biggest axis from being more than MAX_SIZE
+        if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
+            im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
+        im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
+                        interpolation=cv2.INTER_LINEAR)
+        im_scale_factors.append(im_scale)
+        processed_ims.append(im)
+
+    # Create a blob to hold the input images
+    blob = im_list_to_blob(processed_ims)
+
+    return blob, np.array(im_scale_factors)
+
+def _get_rois_blob(im_rois, im_scale_factors):
+    """Converts RoIs into network inputs.
+
+    Arguments:
+        im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
+        im_scale_factors (list): scale factors as returned by _get_image_blob
+
+    Returns:
+        blob (ndarray): R x 5 matrix of RoIs in the image pyramid
+    """
+    rois, levels = _project_im_rois(im_rois, im_scale_factors)
+    rois_blob = np.hstack((levels, rois))
+    return rois_blob.astype(np.float32, copy=False)
+
+def _project_im_rois(im_rois, scales):
+    """Project image RoIs into the image pyramid built by _get_image_blob.
+
+    Arguments:
+        im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
+        scales (list): scale factors as returned by _get_image_blob
+
+    Returns:
+        rois (ndarray): R x 4 matrix of projected RoI coordinates
+        levels (list): image pyramid levels used by each projected RoI
+    """
+    im_rois = im_rois.astype(np.float, copy=False)
+
+    if len(scales) > 1:
+        widths = im_rois[:, 2] - im_rois[:, 0] + 1
+        heights = im_rois[:, 3] - im_rois[:, 1] + 1
+
+        areas = widths * heights
+        scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
+        diff_areas = np.abs(scaled_areas - 224 * 224)
+        levels = diff_areas.argmin(axis=1)[:, np.newaxis]
+    else:
+        levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)
+
+    rois = im_rois * scales[levels]
+
+    return rois, levels
+
+def _get_blobs(im, rois):
+    """Convert an image and RoIs within that image into network inputs."""
+    blobs = {'data' : None, 'rois' : None}
+    blobs['data'], im_scale_factors = _get_image_blob(im)
+    if not cfg.TEST.HAS_RPN:
+        blobs['rois'] = _get_rois_blob(rois, im_scale_factors)
+    return blobs, im_scale_factors
+
+def im_detect(net, im, boxes=None):
+    """Detect object classes in an image given object proposals.
+
+    Arguments:
+        net (caffe.Net): Fast R-CNN network to use
+        im (ndarray): color image to test (in BGR order)
+        boxes (ndarray): R x 4 array of object proposals or None (for RPN)
+
+    Returns:
+        scores (ndarray): R x K array of object class scores (K includes
+            background as object category 0)
+        boxes (ndarray): R x (4*K) array of predicted bounding boxes
+    """
+    blobs, im_scales = _get_blobs(im, boxes)
+
+    # When mapping from image ROIs to feature map ROIs, there's some aliasing
+    # (some distinct image ROIs get mapped to the same feature ROI).
+    # Here, we identify duplicate feature ROIs, so we only compute features
+    # on the unique subset.
+    if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
+        v = np.array([1, 1e3, 1e6, 1e9, 1e12])
+        hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
+        _, index, inv_index = np.unique(hashes, return_index=True,
+                                        return_inverse=True)
+        blobs['rois'] = blobs['rois'][index, :]
+        boxes = boxes[index, :]
+
+    if cfg.TEST.HAS_RPN:
+        im_blob = blobs['data']
+        blobs['im_info'] = np.array(
+            [[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
+            dtype=np.float32)
+
+    # reshape network inputs
+    net.blobs['data'].reshape(*(blobs['data'].shape))
+    if cfg.TEST.HAS_RPN:
+        net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
+    else:
+        net.blobs['rois'].reshape(*(blobs['rois'].shape))
+
+    # do forward
+    forward_kwargs = {'data': blobs['data'].astype(np.float32, copy=False)}
+    if cfg.TEST.HAS_RPN:
+        forward_kwargs['im_info'] = blobs['im_info'].astype(np.float32, copy=False)
+    else:
+        forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False)
+    blobs_out = net.forward(**forward_kwargs)
+
+    if cfg.TEST.HAS_RPN:
+        assert len(im_scales) == 1, "Only single-image batch implemented"
+        rois = net.blobs['rois'].data.copy()
+        # unscale back to raw image space
+        boxes = rois[:, 1:5] / im_scales[0]
+
+    if cfg.TEST.SVM:
+        # use the raw scores before softmax under the assumption they
+        # were trained as linear SVMs
+        scores = net.blobs['cls_score'].data
+    else:
+        # use softmax estimated probabilities
+        scores = blobs_out['cls_prob']
+
+    if cfg.TEST.BBOX_REG:
+        # Apply bounding-box regression deltas
+        box_deltas = blobs_out['bbox_pred']
+        pred_boxes = bbox_transform_inv(boxes, box_deltas)
+        pred_boxes = clip_boxes(pred_boxes, im.shape)
+    else:
+        # Simply repeat the boxes, once for each class
+        pred_boxes = np.tile(boxes, (1, scores.shape[1]))
+
+    if cfg.DEDUP_BOXES > 0 and not cfg.TEST.HAS_RPN:
+        # Map scores and predictions back to the original set of boxes
+        scores = scores[inv_index, :]
+        pred_boxes = pred_boxes[inv_index, :]
+
+    return scores, pred_boxes
+
+def vis_detections(im, class_name, dets, thresh=0.3):
+    """Visual debugging of detections."""
+    import matplotlib.pyplot as plt
+    im = im[:, :, (2, 1, 0)]
+    for i in xrange(np.minimum(10, dets.shape[0])):
+        bbox = dets[i, :4]
+        score = dets[i, -1]
+        if score > thresh:
+            plt.cla()
+            plt.imshow(im)
+            plt.gca().add_patch(
+                plt.Rectangle((bbox[0], bbox[1]),
+                              bbox[2] - bbox[0],
+                              bbox[3] - bbox[1], fill=False,
+                              edgecolor='g', linewidth=3)
+                )
+            plt.title('{}  {:.3f}'.format(class_name, score))
+            plt.show()
+
+def apply_nms(all_boxes, thresh):
+    """Apply non-maximum suppression to all predicted boxes output by the
+    test_net method.
+    """
+    num_classes = len(all_boxes)
+    num_images = len(all_boxes[0])
+    nms_boxes = [[[] for _ in xrange(num_images)]
+                 for _ in xrange(num_classes)]
+    for cls_ind in xrange(num_classes):
+        for im_ind in xrange(num_images):
+            dets = all_boxes[cls_ind][im_ind]
+            if dets == []:
+                continue
+            # CPU NMS is much faster than GPU NMS when the number of boxes
+            # is relative small (e.g., < 10k)
+            # TODO(rbg): autotune NMS dispatch
+            keep = nms(dets, thresh, force_cpu=True)
+            if len(keep) == 0:
+                continue
+            nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
+    return nms_boxes
+
+def test_net(net, imdb, max_per_image=400, thresh=-np.inf, vis=False):
+    """Test a Fast R-CNN network on an image database."""
+    num_images = len(imdb.image_index)
+    # all detections are collected into:
+    #    all_boxes[cls][image] = N x 5 array of detections in
+    #    (x1, y1, x2, y2, score)
+    all_boxes = [[[] for _ in xrange(num_images)]
+                 for _ in xrange(imdb.num_classes)]
+
+    output_dir = get_output_dir(imdb, net)
+
+    # timers
+    _t = {'im_detect' : Timer(), 'misc' : Timer()}
+
+    if not cfg.TEST.HAS_RPN:
+        roidb = imdb.roidb
+
+    for i in xrange(num_images):
+        # filter out any ground truth boxes
+        if cfg.TEST.HAS_RPN:
+            box_proposals = None
+        else:
+            # The roidb may contain ground-truth rois (for example, if the roidb
+            # comes from the training or val split). We only want to evaluate
+            # detection on the *non*-ground-truth rois. We select those the rois
+            # that have the gt_classes field set to 0, which means there's no
+            # ground truth.
+            box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
+
+        im = None
+        if cfg.TEST.FORMAT == "pickle":
+            f = gzip.open(imdb.image_path_at(i), 'rb')
+            im = cPickle.load(f)
+            f.close()
+        else:
+            im = cv2.imread(imdb.image_path_at(i))
+        _t['im_detect'].tic()
+        scores, boxes = im_detect(net, im, box_proposals)
+        _t['im_detect'].toc()
+
+        _t['misc'].tic()
+        # skip j = 0, because it's the background class
+        for j in xrange(1, imdb.num_classes):
+            inds = np.where(scores[:, j] > thresh)[0]
+            cls_scores = scores[inds, j]
+            if cfg.TEST.AGNOSTIC:
+                cls_boxes = boxes[inds, 4:8]
+            else:
+                cls_boxes = boxes[inds, j*4:(j+1)*4]
+            cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
+                .astype(np.float32, copy=False)
+            keep = nms(cls_dets, cfg.TEST.NMS)
+            cls_dets = cls_dets[keep, :]
+            if vis:
+                vis_detections(im, imdb.classes[j], cls_dets)
+            all_boxes[j][i] = cls_dets
+
+        # Limit to max_per_image detections *over all classes*
+        if max_per_image > 0:
+            image_scores = np.hstack([all_boxes[j][i][:, -1]
+                                      for j in xrange(1, imdb.num_classes)])
+            if len(image_scores) > max_per_image:
+                image_thresh = np.sort(image_scores)[-max_per_image]
+                for j in xrange(1, imdb.num_classes):
+                    keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
+                    all_boxes[j][i] = all_boxes[j][i][keep, :]
+        _t['misc'].toc()
+
+        print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
+              .format(i + 1, num_images, _t['im_detect'].average_time,
+                      _t['misc'].average_time)
+
+    det_file = os.path.join(output_dir, 'detections.pkl')
+    with open(det_file, 'wb') as f:
+        cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
+
+    print 'Evaluating detections'
+    imdb.evaluate_detections(all_boxes, output_dir)
diff --git a/examples/rfcn/lib/fast_rcnn/train.py b/examples/rfcn/lib/fast_rcnn/train.py
new file mode 100644
index 000000000..e645f320c
--- /dev/null
+++ b/examples/rfcn/lib/fast_rcnn/train.py
@@ -0,0 +1,210 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Train a Fast R-CNN network."""
+
+import caffe
+from fast_rcnn.config import cfg
+import roi_data_layer.roidb as rdl_roidb
+from utils.timer import Timer
+import numpy as np
+import os
+
+from caffe.proto import caffe_pb2
+import google.protobuf as pb2
+import google.protobuf.text_format
+
+class SolverWrapper(object):
+    """A simple wrapper around Caffe's solver.
+    This wrapper gives us control over he snapshotting process, which we
+    use to unnormalize the learned bounding-box regression weights.
+    """
+
+    def __init__(self, solver_prototxt, roidb, output_dir,
+                 pretrained_model=None):
+        """Initialize the SolverWrapper."""
+        self.output_dir = output_dir
+
+        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
+            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
+            # RPN can only use precomputed normalization because there are no
+            # fixed statistics to compute a priori
+            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
+
+        if cfg.TRAIN.BBOX_REG:
+            print 'Computing bounding-box regression targets...'
+            self.bbox_means, self.bbox_stds = \
+                    rdl_roidb.add_bbox_regression_targets(roidb)
+            print 'done'
+
+        self.solver = caffe.SGDSolver(solver_prototxt)
+        if pretrained_model is not None:
+            print ('Loading pretrained model '
+                   'weights from {:s}').format(pretrained_model)
+            self.solver.net.copy_from(pretrained_model)
+
+        self.solver_param = caffe_pb2.SolverParameter()
+        with open(solver_prototxt, 'rt') as f:
+            pb2.text_format.Merge(f.read(), self.solver_param)
+
+        self.solver.net.layers[0].set_roidb(roidb)
+
+    def snapshot(self):
+        """Take a snapshot of the network after unnormalizing the learned
+        bounding-box regression weights. This enables easy use at test-time.
+        """
+        net = self.solver.net
+
+        scale_bbox_params_faster_rcnn = (cfg.TRAIN.BBOX_REG and
+                             cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
+                             net.params.has_key('bbox_pred'))
+
+        scale_bbox_params_rfcn = (cfg.TRAIN.BBOX_REG and
+                             cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
+                             net.params.has_key('rfcn_bbox'))
+
+        scale_bbox_params_rpn = (cfg.TRAIN.RPN_NORMALIZE_TARGETS and
+                                 net.params.has_key('rpn_bbox_pred'))
+
+        if scale_bbox_params_faster_rcnn:
+            # save original values
+            orig_0 = net.params['bbox_pred'][0].data.copy()
+            orig_1 = net.params['bbox_pred'][1].data.copy()
+
+            # scale and shift with bbox reg unnormalization; then save snapshot
+            net.params['bbox_pred'][0].data[...] = \
+                    (net.params['bbox_pred'][0].data *
+                     self.bbox_stds[:, np.newaxis])
+            net.params['bbox_pred'][1].data[...] = \
+                    (net.params['bbox_pred'][1].data *
+                     self.bbox_stds + self.bbox_means)
+
+        if scale_bbox_params_rpn:
+            rpn_orig_0 = net.params['rpn_bbox_pred'][0].data.copy()
+            rpn_orig_1 = net.params['rpn_bbox_pred'][1].data.copy()
+            num_anchor = rpn_orig_0.shape[0] / 4
+            # scale and shift with bbox reg unnormalization; then save snapshot
+            self.rpn_means = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_MEANS),
+                                      num_anchor)
+            self.rpn_stds = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_STDS),
+                                     num_anchor)
+            net.params['rpn_bbox_pred'][0].data[...] = \
+                (net.params['rpn_bbox_pred'][0].data *
+                 self.rpn_stds[:, np.newaxis, np.newaxis, np.newaxis])
+            net.params['rpn_bbox_pred'][1].data[...] = \
+                (net.params['rpn_bbox_pred'][1].data *
+                 self.rpn_stds + self.rpn_means)
+
+        if scale_bbox_params_rfcn:
+            # save original values
+            orig_0 = net.params['rfcn_bbox'][0].data.copy()
+            orig_1 = net.params['rfcn_bbox'][1].data.copy()
+            repeat = orig_1.shape[0] / self.bbox_means.shape[0]
+
+            # scale and shift with bbox reg unnormalization; then save snapshot
+            net.params['rfcn_bbox'][0].data[...] = \
+                    (net.params['rfcn_bbox'][0].data *
+                     np.repeat(self.bbox_stds, repeat).reshape((orig_1.shape[0], 1, 1, 1)))
+            net.params['rfcn_bbox'][1].data[...] = \
+                    (net.params['rfcn_bbox'][1].data *
+                     np.repeat(self.bbox_stds, repeat) + np.repeat(self.bbox_means, repeat))
+
+        infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
+                 if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
+        filename = (self.solver_param.snapshot_prefix + infix +
+                    '_iter_{:d}'.format(self.solver.iter) + '.caffemodel')
+        filename = os.path.join(self.output_dir, filename)
+
+        net.save(str(filename))
+        print 'Wrote snapshot to: {:s}'.format(filename)
+
+        if scale_bbox_params_faster_rcnn:
+            # restore net to original state
+            net.params['bbox_pred'][0].data[...] = orig_0
+            net.params['bbox_pred'][1].data[...] = orig_1
+        if scale_bbox_params_rfcn:
+            # restore net to original state
+            net.params['rfcn_bbox'][0].data[...] = orig_0
+            net.params['rfcn_bbox'][1].data[...] = orig_1
+        if scale_bbox_params_rpn:
+            # restore net to original state
+            net.params['rpn_bbox_pred'][0].data[...] = rpn_orig_0
+            net.params['rpn_bbox_pred'][1].data[...] = rpn_orig_1
+
+        return filename
+
+    def train_model(self, max_iters):
+        """Network training loop."""
+        last_snapshot_iter = -1
+        timer = Timer()
+        model_paths = []
+        while self.solver.iter < max_iters:
+            # Make one SGD update
+            timer.tic()
+            self.solver.step(1)
+            timer.toc()
+            if self.solver.iter % (10 * self.solver_param.display) == 0:
+                print 'speed: {:.3f}s / iter'.format(timer.average_time)
+                print 'fps: {:.3f}'.format(1.0 / timer.average_time)
+
+            if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
+                last_snapshot_iter = self.solver.iter
+                model_paths.append(self.snapshot())
+
+        if last_snapshot_iter != self.solver.iter:
+            model_paths.append(self.snapshot())
+        return model_paths
+
+def get_training_roidb(imdb):
+    """Returns a roidb (Region of Interest database) for use in training."""
+    if cfg.TRAIN.USE_FLIPPED:
+        print 'Appending horizontally-flipped training examples...'
+        imdb.append_flipped_images()
+        print 'done'
+
+    print 'Preparing training data...'
+    rdl_roidb.prepare_roidb(imdb)
+    print 'done'
+
+    return imdb.roidb
+
+def filter_roidb(roidb):
+    """Remove roidb entries that have no usable RoIs."""
+
+    def is_valid(entry):
+        # Valid images have:
+        #   (1) At least one foreground RoI OR
+        #   (2) At least one background RoI
+        overlaps = entry['max_overlaps']
+        # find boxes with sufficient overlap
+        fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
+        # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
+        bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
+                           (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
+        # image is only valid if such boxes exist
+        valid = len(fg_inds) > 0 or len(bg_inds) > 0
+        return valid
+
+    num = len(roidb)
+    filtered_roidb = [entry for entry in roidb if is_valid(entry)]
+    num_after = len(filtered_roidb)
+    print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
+                                                       num, num_after)
+    return filtered_roidb
+
+def train_net(solver_prototxt, roidb, output_dir,
+              pretrained_model=None, max_iters=40000):
+    """Train a Fast R-CNN network."""
+
+    roidb = filter_roidb(roidb)
+    sw = SolverWrapper(solver_prototxt, roidb, output_dir,
+                       pretrained_model=pretrained_model)
+
+    print 'Solving...'
+    model_paths = sw.train_model(max_iters)
+    print 'done solving'
+    return model_paths
diff --git a/examples/rfcn/lib/nms/__init__.py b/examples/rfcn/lib/nms/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/rfcn/lib/nms/cpu_nms.pyx b/examples/rfcn/lib/nms/cpu_nms.pyx
new file mode 100644
index 000000000..625c42df3
--- /dev/null
+++ b/examples/rfcn/lib/nms/cpu_nms.pyx
@@ -0,0 +1,91 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+cimport numpy as np
+cimport cython
+cimport openmp
+
+from cython.parallel cimport prange
+from cython.parallel cimport parallel
+
+cdef inline np.float32_t max(np.float32_t a, np.float32_t b) nogil:
+    return a if a >= b else b
+
+cdef inline np.float32_t min(np.float32_t a, np.float32_t b) nogil:
+    return a if a <= b else b
+
+cdef inline np.int_t thresholding(np.float32_t ovr, np.float32_t thresh) nogil:
+    return 1 if ovr >= thresh else 0
+
+import os
+from multiprocessing import cpu_count
+cdef int set_num = 0
+try:
+    set_num = os.environ["OMP_NUM_THREADS"]
+except:
+    set_num = cpu_count() / 2
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):
+    cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0]
+    cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1]
+    cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2]
+    cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3]
+    cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4]
+
+    cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1]
+
+    cdef int ndets = dets.shape[0]
+    cdef np.ndarray[np.int_t, ndim=1] suppressed = \
+            np.zeros((ndets), dtype=np.int)
+
+    # nominal indices
+    cdef int _i, _j
+    # sorted indices
+    cdef int i, j
+    # temp variables for box i's (the box currently under consideration)
+    cdef np.float32_t ix1, iy1, ix2, iy2, iarea
+    # variables for computing overlap with box j (lower scoring box)
+    cdef np.float32_t xx1, yy1, xx2, yy2
+    cdef np.float32_t w, h
+    cdef np.float32_t inter, ovr
+    cdef np.float32_t threshc = thresh
+
+    global set_num
+
+    keep = []
+    for _i in range(ndets):
+        i = order[_i]
+        if suppressed[i] == 1:
+            continue
+        keep.append(i)
+        ix1 = x1[i]
+        iy1 = y1[i]
+        ix2 = x2[i]
+        iy2 = y2[i]
+        iarea = areas[i]
+        thread_num = set_num if (ndets - _i - 1) > set_num else (ndets - _i - 1)
+        if thread_num == 0:
+            continue
+        with nogil:
+            for _j in prange(_i + 1, ndets, schedule = 'dynamic', num_threads = thread_num):
+                j = order[_j]
+                if suppressed[j] == 0:
+                    xx1 = max(ix1, x1[j])
+                    yy1 = max(iy1, y1[j])
+                    xx2 = min(ix2, x2[j])
+                    yy2 = min(iy2, y2[j])
+                    w = max(0.0, xx2 - xx1 + 1)
+                    h = max(0.0, yy2 - yy1 + 1)
+                    inter = w * h
+                    ovr = inter / (iarea + areas[j] - inter)
+                    suppressed[j] = thresholding(ovr, threshc)
+
+    return keep
diff --git a/examples/rfcn/lib/nms/gpu_nms.hpp b/examples/rfcn/lib/nms/gpu_nms.hpp
new file mode 100644
index 000000000..68b6d42cd
--- /dev/null
+++ b/examples/rfcn/lib/nms/gpu_nms.hpp
@@ -0,0 +1,2 @@
+void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num,
+          int boxes_dim, float nms_overlap_thresh, int device_id);
diff --git a/examples/rfcn/lib/nms/gpu_nms.pyx b/examples/rfcn/lib/nms/gpu_nms.pyx
new file mode 100644
index 000000000..59d84afe9
--- /dev/null
+++ b/examples/rfcn/lib/nms/gpu_nms.pyx
@@ -0,0 +1,31 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+cimport numpy as np
+
+assert sizeof(int) == sizeof(np.int32_t)
+
+cdef extern from "gpu_nms.hpp":
+    void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int)
+
+def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh,
+            np.int32_t device_id=0):
+    cdef int boxes_num = dets.shape[0]
+    cdef int boxes_dim = dets.shape[1]
+    cdef int num_out
+    cdef np.ndarray[np.int32_t, ndim=1] \
+        keep = np.zeros(boxes_num, dtype=np.int32)
+    cdef np.ndarray[np.float32_t, ndim=1] \
+        scores = dets[:, 4]
+    cdef np.ndarray[np.int_t, ndim=1] \
+        order = scores.argsort()[::-1]
+    cdef np.ndarray[np.float32_t, ndim=2] \
+        sorted_dets = dets[order, :]
+    _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id)
+    keep = keep[:num_out]
+    return list(order[keep])
diff --git a/examples/rfcn/lib/nms/nms_kernel.cu b/examples/rfcn/lib/nms/nms_kernel.cu
new file mode 100644
index 000000000..038a59012
--- /dev/null
+++ b/examples/rfcn/lib/nms/nms_kernel.cu
@@ -0,0 +1,144 @@
+// ------------------------------------------------------------------
+// Faster R-CNN
+// Copyright (c) 2015 Microsoft
+// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
+// Written by Shaoqing Ren
+// ------------------------------------------------------------------
+
+#include "gpu_nms.hpp"
+#include <vector>
+#include <iostream>
+
+#define CUDA_CHECK(condition) \
+  /* Code block avoids redefinition of cudaError_t error */ \
+  do { \
+    cudaError_t error = condition; \
+    if (error != cudaSuccess) { \
+      std::cout << cudaGetErrorString(error) << std::endl; \
+    } \
+  } while (0)
+
+#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+int const threadsPerBlock = sizeof(unsigned long long) * 8;
+
+__device__ inline float devIoU(float const * const a, float const * const b) {
+  float left = max(a[0], b[0]), right = min(a[2], b[2]);
+  float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
+  float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
+  float interS = width * height;
+  float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
+  float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
+  return interS / (Sa + Sb - interS);
+}
+
+__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
+                           const float *dev_boxes, unsigned long long *dev_mask) {
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size =
+        min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
+  const int col_size =
+        min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
+
+  __shared__ float block_boxes[threadsPerBlock * 5];
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
+    const float *cur_box = dev_boxes + cur_box_idx * 5;
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
+    }
+    for (i = start; i < col_size; i++) {
+      if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = DIVUP(n_boxes, threadsPerBlock);
+    dev_mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+void _set_device(int device_id) {
+  int current_device;
+  CUDA_CHECK(cudaGetDevice(&current_device));
+  if (current_device == device_id) {
+    return;
+  }
+  // The call to cudaSetDevice must come before any calls to Get, which
+  // may perform initialization using the GPU.
+  CUDA_CHECK(cudaSetDevice(device_id));
+}
+
+void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num,
+          int boxes_dim, float nms_overlap_thresh, int device_id) {
+  _set_device(device_id);
+
+  float* boxes_dev = NULL;
+  unsigned long long* mask_dev = NULL;
+
+  const int col_blocks = DIVUP(boxes_num, threadsPerBlock);
+
+  CUDA_CHECK(cudaMalloc(&boxes_dev,
+                        boxes_num * boxes_dim * sizeof(float)));
+  CUDA_CHECK(cudaMemcpy(boxes_dev,
+                        boxes_host,
+                        boxes_num * boxes_dim * sizeof(float),
+                        cudaMemcpyHostToDevice));
+
+  CUDA_CHECK(cudaMalloc(&mask_dev,
+                        boxes_num * col_blocks * sizeof(unsigned long long)));
+
+  dim3 blocks(DIVUP(boxes_num, threadsPerBlock),
+              DIVUP(boxes_num, threadsPerBlock));
+  dim3 threads(threadsPerBlock);
+  nms_kernel<<<blocks, threads>>>(boxes_num,
+                                  nms_overlap_thresh,
+                                  boxes_dev,
+                                  mask_dev);
+
+  std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
+  CUDA_CHECK(cudaMemcpy(&mask_host[0],
+                        mask_dev,
+                        sizeof(unsigned long long) * boxes_num * col_blocks,
+                        cudaMemcpyDeviceToHost));
+
+  std::vector<unsigned long long> remv(col_blocks);
+  memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
+
+  int num_to_keep = 0;
+  for (int i = 0; i < boxes_num; i++) {
+    int nblock = i / threadsPerBlock;
+    int inblock = i % threadsPerBlock;
+
+    if (!(remv[nblock] & (1ULL << inblock))) {
+      keep_out[num_to_keep++] = i;
+      unsigned long long *p = &mask_host[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++) {
+        remv[j] |= p[j];
+      }
+    }
+  }
+  *num_out = num_to_keep;
+
+  CUDA_CHECK(cudaFree(boxes_dev));
+  CUDA_CHECK(cudaFree(mask_dev));
+}
diff --git a/examples/rfcn/lib/nms/py_cpu_nms.py b/examples/rfcn/lib/nms/py_cpu_nms.py
new file mode 100644
index 000000000..54e7b25fe
--- /dev/null
+++ b/examples/rfcn/lib/nms/py_cpu_nms.py
@@ -0,0 +1,38 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    x2 = dets[:, 2]
+    y2 = dets[:, 3]
+    scores = dets[:, 4]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
diff --git a/examples/rfcn/lib/pycocotools/UPSTREAM_REV b/examples/rfcn/lib/pycocotools/UPSTREAM_REV
new file mode 100644
index 000000000..706219b77
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/UPSTREAM_REV
@@ -0,0 +1 @@
+https://github.com/pdollar/coco/commit/3ac47c77ebd5a1ed4254a98b7fbf2ef4765a3574
diff --git a/examples/rfcn/lib/pycocotools/__init__.py b/examples/rfcn/lib/pycocotools/__init__.py
new file mode 100644
index 000000000..3f7d85bba
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/__init__.py
@@ -0,0 +1 @@
+__author__ = 'tylin'
diff --git a/examples/rfcn/lib/pycocotools/_mask.c b/examples/rfcn/lib/pycocotools/_mask.c
new file mode 100644
index 000000000..e47a984fc
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/_mask.c
@@ -0,0 +1,15550 @@
+/* Generated by Cython 0.25.2 */
+
+#define PY_SSIZE_T_CLEAN
+#include "Python.h"
+#ifndef Py_PYTHON_H
+    #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000)
+    #error Cython requires Python 2.6+ or Python 3.2+.
+#else
+#define CYTHON_ABI "0_25_2"
+#include <stddef.h>
+#ifndef offsetof
+  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#ifndef HAVE_LONG_LONG
+  #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000)
+    #define HAVE_LONG_LONG
+  #endif
+#endif
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+  #define CYTHON_COMPILING_IN_PYPY 1
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #undef CYTHON_USE_TYPE_SLOTS
+  #define CYTHON_USE_TYPE_SLOTS 0
+  #undef CYTHON_USE_ASYNC_SLOTS
+  #define CYTHON_USE_ASYNC_SLOTS 0
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_INTERNALS
+  #define CYTHON_USE_UNICODE_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #undef CYTHON_AVOID_BORROWED_REFS
+  #define CYTHON_AVOID_BORROWED_REFS 1
+  #undef CYTHON_ASSUME_SAFE_MACROS
+  #define CYTHON_ASSUME_SAFE_MACROS 0
+  #undef CYTHON_UNPACK_METHODS
+  #define CYTHON_UNPACK_METHODS 0
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+#elif defined(PYSTON_VERSION)
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 1
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #undef CYTHON_USE_ASYNC_SLOTS
+  #define CYTHON_USE_ASYNC_SLOTS 0
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+#else
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 1
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #if PY_MAJOR_VERSION < 3
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYLONG_INTERNALS
+    #define CYTHON_USE_PYLONG_INTERNALS 0
+  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
+    #define CYTHON_USE_PYLONG_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_PYLIST_INTERNALS
+    #define CYTHON_USE_PYLIST_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #if PY_VERSION_HEX < 0x030300F0
+    #undef CYTHON_USE_UNICODE_WRITER
+    #define CYTHON_USE_UNICODE_WRITER 0
+  #elif !defined(CYTHON_USE_UNICODE_WRITER)
+    #define CYTHON_USE_UNICODE_WRITER 1
+  #endif
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #ifndef CYTHON_FAST_THREAD_STATE
+    #define CYTHON_FAST_THREAD_STATE 1
+  #endif
+  #ifndef CYTHON_FAST_PYCALL
+    #define CYTHON_FAST_PYCALL 1
+  #endif
+#endif
+#if !defined(CYTHON_FAST_PYCCALL)
+#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
+#endif
+#if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+  #undef SHIFT
+  #undef BASE
+  #undef MASK
+#endif
+#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
+  #define Py_OptimizeFlag 0
+#endif
+#define __PYX_BUILD_PY_SSIZE_T "n"
+#define CYTHON_FORMAT_SSIZE_T "z"
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyType_Type
+#endif
+#ifndef Py_TPFLAGS_CHECKTYPES
+  #define Py_TPFLAGS_CHECKTYPES 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_INDEX
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
+  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_FINALIZE
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#ifndef METH_FASTCALL
+  #define METH_FASTCALL 0x80
+  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args,
+                                              Py_ssize_t nargs, PyObject *kwnames);
+#else
+  #define __Pyx_PyCFunctionFast _PyCFunctionFast
+#endif
+#if CYTHON_FAST_PYCCALL
+#define __Pyx_PyFastCFunction_Check(func)\
+    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)))))
+#else
+#define __Pyx_PyFastCFunction_Check(func) 0
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
+                                              0 : _PyUnicode_Ready((PyObject *)(op)))
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
+  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define PyUnicode_1BYTE_KIND  1
+  #define PyUnicode_2BYTE_KIND  2
+  #define PyUnicode_4BYTE_KIND  4
+  #define __Pyx_PyUnicode_READY(op)       (0)
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
+#else
+  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
+      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
+  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
+  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
+  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
+  #define PyObject_Malloc(s)   PyMem_Malloc(s)
+  #define PyObject_Free(p)     PyMem_Free(p)
+  #define PyObject_Realloc(p)  PyMem_Realloc(p)
+#endif
+#if CYTHON_COMPILING_IN_PYSTON
+  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
+#else
+  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
+  #define PyObject_ASCII(o)            PyObject_Repr(o)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyStringObject               PyUnicodeObject
+  #define PyString_Type                PyUnicode_Type
+  #define PyString_Check               PyUnicode_Check
+  #define PyString_CheckExact          PyUnicode_CheckExact
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
+#endif
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+#if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
+  #ifndef PyUnicode_InternFromString
+    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
+  #endif
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+  typedef long Py_hash_t;
+  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
+#else
+  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
+#else
+  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
+#endif
+#if CYTHON_USE_ASYNC_SLOTS
+  #if PY_VERSION_HEX >= 0x030500B1
+    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
+    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
+  #else
+    typedef struct {
+        unaryfunc am_await;
+        unaryfunc am_aiter;
+        unaryfunc am_anext;
+    } __Pyx_PyAsyncMethodsStruct;
+    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
+  #endif
+#else
+  #define __Pyx_PyType_AsAsync(obj) NULL
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+#ifndef CYTHON_MAYBE_UNUSED_VAR
+#  if defined(__cplusplus)
+     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
+#  else
+#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
+#  endif
+#endif
+#ifndef CYTHON_NCP_UNUSED
+# if CYTHON_COMPILING_IN_CPYTHON
+#  define CYTHON_NCP_UNUSED
+# else
+#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
+# endif
+#endif
+#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
+
+#ifndef CYTHON_INLINE
+  #if defined(__clang__)
+    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
+  #elif defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+  #define _USE_MATH_DEFINES
+#endif
+#include <math.h>
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+  float value;
+  memset(&value, 0xFF, sizeof(value));
+  return value;
+}
+#endif
+#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
+#define __Pyx_truncl trunc
+#else
+#define __Pyx_truncl truncl
+#endif
+
+
+#define __PYX_ERR(f_index, lineno, Ln_error) \
+{ \
+  __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
+}
+
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+
+#ifndef __PYX_EXTERN_C
+  #ifdef __cplusplus
+    #define __PYX_EXTERN_C extern "C"
+  #else
+    #define __PYX_EXTERN_C extern
+  #endif
+#endif
+
+#define __PYX_HAVE__pycocotools___mask
+#define __PYX_HAVE_API__pycocotools___mask
+#include <string.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include "numpy/arrayobject.h"
+#include "numpy/ufuncobject.h"
+#include "maskApi.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif /* _OPENMP */
+
+#ifdef PYREX_WITHOUT_ASSERTIONS
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
+                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_uchar_cast(c) ((unsigned char)c)
+#define __Pyx_long_cast(x) ((long)x)
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
+    (sizeof(type) < sizeof(Py_ssize_t))  ||\
+    (sizeof(type) > sizeof(Py_ssize_t) &&\
+          likely(v < (type)PY_SSIZE_T_MAX ||\
+                 v == (type)PY_SSIZE_T_MAX)  &&\
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
+                                v == (type)PY_SSIZE_T_MIN)))  ||\
+    (sizeof(type) == sizeof(Py_ssize_t) &&\
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
+                               v == (type)PY_SSIZE_T_MAX)))  )
+#if defined (__cplusplus) && __cplusplus >= 201103L
+    #include <cstdlib>
+    #define __Pyx_sst_abs(value) std::abs(value)
+#elif SIZEOF_INT >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) abs(value)
+#elif SIZEOF_LONG >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) labs(value)
+#elif defined (_MSC_VER) && defined (_M_X64)
+    #define __Pyx_sst_abs(value) _abs64(value)
+#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define __Pyx_sst_abs(value) llabs(value)
+#elif defined (__GNUC__)
+    #define __Pyx_sst_abs(value) __builtin_llabs(value)
+#else
+    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
+#endif
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString        PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+#if PY_MAJOR_VERSION < 3
+    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
+#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
+#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
+#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
+#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
+#if PY_MAJOR_VERSION < 3
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
+{
+    const Py_UNICODE *u_end = u;
+    while (*u_end++) ;
+    return (size_t)(u_end - u - 1);
+}
+#else
+#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
+#endif
+#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
+#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
+#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
+#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_ASSUME_SAFE_MACROS
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
+#else
+#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
+#endif
+#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    PyObject* ascii_chars_u = NULL;
+    PyObject* ascii_chars_b = NULL;
+    const char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    if (strcmp(default_encoding_c, "ascii") == 0) {
+        __Pyx_sys_getdefaultencoding_not_ascii = 0;
+    } else {
+        char ascii_chars[128];
+        int c;
+        for (c = 0; c < 128; c++) {
+            ascii_chars[c] = c;
+        }
+        __Pyx_sys_getdefaultencoding_not_ascii = 1;
+        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+        if (!ascii_chars_u) goto bad;
+        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+            PyErr_Format(
+                PyExc_ValueError,
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+                default_encoding_c);
+            goto bad;
+        }
+        Py_DECREF(ascii_chars_u);
+        Py_DECREF(ascii_chars_b);
+    }
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
+    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    return -1;
+}
+#endif
+#endif
+
+
+/* Test for GCC > 2.95 */
+#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
+  #define likely(x)   __builtin_expect(!!(x), 1)
+  #define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* !__GNUC__ or GCC < 2.95 */
+  #define likely(x)   (x)
+  #define unlikely(x) (x)
+#endif /* __GNUC__ */
+
+static PyObject *__pyx_m;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static PyObject *__pyx_empty_unicode;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+/* Header.proto */
+#if !defined(CYTHON_CCOMPLEX)
+  #if defined(__cplusplus)
+    #define CYTHON_CCOMPLEX 1
+  #elif defined(_Complex_I)
+    #define CYTHON_CCOMPLEX 1
+  #else
+    #define CYTHON_CCOMPLEX 0
+  #endif
+#endif
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #include <complex>
+  #else
+    #include <complex.h>
+  #endif
+#endif
+#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
+  #undef _Complex_I
+  #define _Complex_I 1.0fj
+#endif
+
+
+static const char *__pyx_f[] = {
+  "pycocotools/_mask.pyx",
+  "__init__.pxd",
+  "type.pxd",
+};
+/* BufferFormatStructs.proto */
+#define IS_UNSIGNED(type) (((type) -1) > 0)
+struct __Pyx_StructField_;
+#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
+typedef struct {
+  const char* name;
+  struct __Pyx_StructField_* fields;
+  size_t size;
+  size_t arraysize[8];
+  int ndim;
+  char typegroup;
+  char is_unsigned;
+  int flags;
+} __Pyx_TypeInfo;
+typedef struct __Pyx_StructField_ {
+  __Pyx_TypeInfo* type;
+  const char* name;
+  size_t offset;
+} __Pyx_StructField;
+typedef struct {
+  __Pyx_StructField* field;
+  size_t parent_offset;
+} __Pyx_BufFmt_StackElem;
+typedef struct {
+  __Pyx_StructField root;
+  __Pyx_BufFmt_StackElem* head;
+  size_t fmt_offset;
+  size_t new_count, enc_count;
+  size_t struct_alignment;
+  int is_complex;
+  char enc_type;
+  char new_packmode;
+  char enc_packmode;
+  char is_valid_array;
+} __Pyx_BufFmt_Context;
+
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725
+ * # in Cython to enable them only on the right systems.
+ * 
+ * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ */
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726
+ * 
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t
+ */
+typedef npy_int16 __pyx_t_5numpy_int16_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int64      int64_t
+ * #ctypedef npy_int96      int96_t
+ */
+typedef npy_int32 __pyx_t_5numpy_int32_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_int96      int96_t
+ * #ctypedef npy_int128     int128_t
+ */
+typedef npy_int64 __pyx_t_5numpy_int64_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732
+ * #ctypedef npy_int128     int128_t
+ * 
+ * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ */
+typedef npy_uint8 __pyx_t_5numpy_uint8_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733
+ * 
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t
+ */
+typedef npy_uint16 __pyx_t_5numpy_uint16_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint64     uint64_t
+ * #ctypedef npy_uint96     uint96_t
+ */
+typedef npy_uint32 __pyx_t_5numpy_uint32_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_uint96     uint96_t
+ * #ctypedef npy_uint128    uint128_t
+ */
+typedef npy_uint64 __pyx_t_5numpy_uint64_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739
+ * #ctypedef npy_uint128    uint128_t
+ * 
+ * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_float64    float64_t
+ * #ctypedef npy_float80    float80_t
+ */
+typedef npy_float32 __pyx_t_5numpy_float32_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740
+ * 
+ * ctypedef npy_float32    float32_t
+ * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_float80    float80_t
+ * #ctypedef npy_float128   float128_t
+ */
+typedef npy_float64 __pyx_t_5numpy_float64_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749
+ * # The int types are mapped a bit surprising --
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t
+ */
+typedef npy_long __pyx_t_5numpy_int_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   longlong_t
+ * 
+ */
+typedef npy_longlong __pyx_t_5numpy_long_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_ulong      uint_t
+ */
+typedef npy_longlong __pyx_t_5numpy_longlong_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753
+ * ctypedef npy_longlong   longlong_t
+ * 
+ * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t
+ */
+typedef npy_ulong __pyx_t_5numpy_uint_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754
+ * 
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_intp       intp_t
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":757
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uintp      uintp_t
+ * 
+ */
+typedef npy_intp __pyx_t_5numpy_intp_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758
+ * 
+ * ctypedef npy_intp       intp_t
+ * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_double     float_t
+ */
+typedef npy_uintp __pyx_t_5numpy_uintp_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760
+ * ctypedef npy_uintp      uintp_t
+ * 
+ * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t
+ */
+typedef npy_double __pyx_t_5numpy_float_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":761
+ * 
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ */
+typedef npy_double __pyx_t_5numpy_double_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ */
+typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
+/* Declarations.proto */
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    typedef ::std::complex< float > __pyx_t_float_complex;
+  #else
+    typedef float _Complex __pyx_t_float_complex;
+  #endif
+#else
+    typedef struct { float real, imag; } __pyx_t_float_complex;
+#endif
+static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+
+/* Declarations.proto */
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    typedef ::std::complex< double > __pyx_t_double_complex;
+  #else
+    typedef double _Complex __pyx_t_double_complex;
+  #endif
+#else
+    typedef struct { double real, imag; } __pyx_t_double_complex;
+#endif
+static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
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+
+/*--- Type declarations ---*/
+struct __pyx_obj_11pycocotools_5_mask_RLEs;
+struct __pyx_obj_11pycocotools_5_mask_Masks;
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+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t
+ */
+typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ */
+typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cdouble     complex_t
+ */
+typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
+
+/* "../../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":768
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):
+ */
+typedef npy_cdouble __pyx_t_5numpy_complex_t;
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+/* "pycocotools/_mask.pyx":53
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+ * # the class handles the memory allocation and deallocation
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+ *     cdef siz _n
+ */
+struct __pyx_obj_11pycocotools_5_mask_RLEs {
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+  RLE *_R;
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+
+
+/* "pycocotools/_mask.pyx":74
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+  PyObject_HEAD
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+  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
+  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
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+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
+  #define __Pyx_RefNannyFinishContext()
+  #define __Pyx_INCREF(r) Py_INCREF(r)
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+  #define __Pyx_GOTREF(r)
+  #define __Pyx_GIVEREF(r)
+  #define __Pyx_XINCREF(r) Py_XINCREF(r)
+  #define __Pyx_XDECREF(r) Py_XDECREF(r)
+  #define __Pyx_XGOTREF(r)
+  #define __Pyx_XGIVEREF(r)
+#endif
+#define __Pyx_XDECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
+        r = v; __Pyx_XDECREF(tmp);\
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+#define __Pyx_DECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
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+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+/* PyObjectGetAttrStr.proto */
+#if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro))
+        return tp->tp_getattro(obj, attr_name);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_getattr))
+        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
+#endif
+    return PyObject_GetAttr(obj, attr_name);
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+#else
+#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
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+
+/* GetBuiltinName.proto */
+static PyObject *__Pyx_GetBuiltinName(PyObject *name);
+
+/* RaiseDoubleKeywords.proto */
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
+
+/* ParseKeywords.proto */
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
+    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\
+    const char* function_name);
+
+/* RaiseArgTupleInvalid.proto */
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
+
+/* IncludeStringH.proto */
+#include <string.h>
+
+/* BytesEquals.proto */
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
+
+/* UnicodeEquals.proto */
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
+
+/* StrEquals.proto */
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
+#else
+#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
+#endif
+
+/* PyObjectCall.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
+#else
+#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
+#endif
+
+/* PyThreadStateGet.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;
+#define __Pyx_PyThreadState_assign  __pyx_tstate = PyThreadState_GET();
+#else
+#define __Pyx_PyThreadState_declare
+#define __Pyx_PyThreadState_assign
+#endif
+
+/* PyErrFetchRestore.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
+#endif
+
+/* RaiseException.proto */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
+/* ExtTypeTest.proto */
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
+
+/* ArgTypeTest.proto */
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+    const char *name, int exact);
+
+/* ListAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        Py_SIZE(list) = len+1;
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* PyIntBinop.proto */
+#if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
+#else
+#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\
+    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
+#endif
+
+/* GetItemInt.proto */
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
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+               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
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+
+/* BufferFormatCheck.proto */
+static CYTHON_INLINE int  __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
+    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type); // PROTO
+
+/* GetModuleGlobalName.proto */
+static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);
+
+/* PyCFunctionFastCall.proto */
+#if CYTHON_FAST_PYCCALL
+static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);
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+#define __Pyx_PyCFunction_FastCall(func, args, nargs)  (assert(0), NULL)
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+
+/* PyFunctionFastCall.proto */
+#if CYTHON_FAST_PYCALL
+#define __Pyx_PyFunction_FastCall(func, args, nargs)\
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+#if 1 || PY_VERSION_HEX < 0x030600B1
+static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs);
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+#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)
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+
+/* PyObjectCallMethO.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
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+/* PyObjectCallOneArg.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
+
+/* PyIntBinop.proto */
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+static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
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+#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\
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+
+/* ListCompAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len)) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        Py_SIZE(list) = len+1;
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* FetchCommonType.proto */
+static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
+
+/* CythonFunction.proto */
+#define __Pyx_CyFunction_USED 1
+#include <structmember.h>
+#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
+#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
+#define __Pyx_CYFUNCTION_CCLASS        0x04
+#define __Pyx_CyFunction_GetClosure(f)\
+    (((__pyx_CyFunctionObject *) (f))->func_closure)
+#define __Pyx_CyFunction_GetClassObj(f)\
+    (((__pyx_CyFunctionObject *) (f))->func_classobj)
+#define __Pyx_CyFunction_Defaults(type, f)\
+    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
+#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
+    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
+typedef struct {
+    PyCFunctionObject func;
+#if PY_VERSION_HEX < 0x030500A0
+    PyObject *func_weakreflist;
+#endif
+    PyObject *func_dict;
+    PyObject *func_name;
+    PyObject *func_qualname;
+    PyObject *func_doc;
+    PyObject *func_globals;
+    PyObject *func_code;
+    PyObject *func_closure;
+    PyObject *func_classobj;
+    void *defaults;
+    int defaults_pyobjects;
+    int flags;
+    PyObject *defaults_tuple;
+    PyObject *defaults_kwdict;
+    PyObject *(*defaults_getter)(PyObject *);
+    PyObject *func_annotations;
+} __pyx_CyFunctionObject;
+static PyTypeObject *__pyx_CyFunctionType = 0;
+#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\
+    __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code)
+static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml,
+                                      int flags, PyObject* qualname,
+                                      PyObject *self,
+                                      PyObject *module, PyObject *globals,
+                                      PyObject* code);
+static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
+                                                         size_t size,
+                                                         int pyobjects);
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
+                                                            PyObject *tuple);
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
+                                                             PyObject *dict);
+static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
+                                                              PyObject *dict);
+static int __pyx_CyFunction_init(void);
+
+/* BufferFallbackError.proto */
+static void __Pyx_RaiseBufferFallbackError(void);
+
+/* None.proto */
+static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);
+
+/* BufferIndexError.proto */
+static void __Pyx_RaiseBufferIndexError(int axis);
+
+#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
+/* DictGetItem.proto */
+#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
+static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
+    PyObject *value;
+    value = PyDict_GetItemWithError(d, key);
+    if (unlikely(!value)) {
+        if (!PyErr_Occurred()) {
+            PyObject* args = PyTuple_Pack(1, key);
+            if (likely(args))
+                PyErr_SetObject(PyExc_KeyError, args);
+            Py_XDECREF(args);
+        }
+        return NULL;
+    }
+    Py_INCREF(value);
+    return value;
+}
+#else
+    #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
+#endif
+
+/* RaiseTooManyValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
+/* RaiseNeedMoreValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+/* RaiseNoneIterError.proto */
+static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
+
+/* SaveResetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+#else
+#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
+#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
+#endif
+
+/* PyErrExceptionMatches.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
+#else
+#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
+#endif
+
+/* GetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* Import.proto */
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
+
+/* CodeObjectCache.proto */
+typedef struct {
+    PyCodeObject* code_object;
+    int code_line;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+/* AddTraceback.proto */
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename);
+
+/* BufferStructDeclare.proto */
+typedef struct {
+  Py_ssize_t shape, strides, suboffsets;
+} __Pyx_Buf_DimInfo;
+typedef struct {
+  size_t refcount;
+  Py_buffer pybuffer;
+} __Pyx_Buffer;
+typedef struct {
+  __Pyx_Buffer *rcbuffer;
+  char *data;
+  __Pyx_Buf_DimInfo diminfo[8];
+} __Pyx_LocalBuf_ND;
+
+#if PY_MAJOR_VERSION < 3
+    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
+    static void __Pyx_ReleaseBuffer(Py_buffer *view);
+#else
+    #define __Pyx_GetBuffer PyObject_GetBuffer
+    #define __Pyx_ReleaseBuffer PyBuffer_Release
+#endif
+
+
+/* None.proto */
+static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0};
+static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1};
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value);
+
+/* RealImag.proto */
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #define __Pyx_CREAL(z) ((z).real())
+    #define __Pyx_CIMAG(z) ((z).imag())
+  #else
+    #define __Pyx_CREAL(z) (__real__(z))
+    #define __Pyx_CIMAG(z) (__imag__(z))
+  #endif
+#else
+    #define __Pyx_CREAL(z) ((z).real)
+    #define __Pyx_CIMAG(z) ((z).imag)
+#endif
+#if defined(__cplusplus) && CYTHON_CCOMPLEX\
+        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
+    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
+    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
+#else
+    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
+    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
+#endif
+
+/* Arithmetic.proto */
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
+    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
+    #define __Pyx_c_diff_float(a, b) ((a)-(b))
+    #define __Pyx_c_prod_float(a, b) ((a)*(b))
+    #define __Pyx_c_quot_float(a, b) ((a)/(b))
+    #define __Pyx_c_neg_float(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
+    #define __Pyx_c_conj_float(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_abs_float(z)     (::std::abs(z))
+        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zero_float(z) ((z)==0)
+    #define __Pyx_c_conj_float(z)    (conjf(z))
+    #if 1
+        #define __Pyx_c_abs_float(z)     (cabsf(z))
+        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    #endif
+#endif
+
+/* Arithmetic.proto */
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
+    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
+    #define __Pyx_c_diff_double(a, b) ((a)-(b))
+    #define __Pyx_c_prod_double(a, b) ((a)*(b))
+    #define __Pyx_c_quot_double(a, b) ((a)/(b))
+    #define __Pyx_c_neg_double(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
+    #define __Pyx_c_conj_double(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_abs_double(z)     (::std::abs(z))
+        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zero_double(z) ((z)==0)
+    #define __Pyx_c_conj_double(z)    (conj(z))
+    #if 1
+        #define __Pyx_c_abs_double(z)     (cabs(z))
+        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    #endif
+#endif
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+/* CheckBinaryVersion.proto */
+static int __Pyx_check_binary_version(void);
+
+/* PyIdentifierFromString.proto */
+#if !defined(__Pyx_PyIdentifier_FromString)
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
+#else
+  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
+#endif
+#endif
+
+/* ModuleImport.proto */
+static PyObject *__Pyx_ImportModule(const char *name);
+
+/* TypeImport.proto */
+static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);
+
+/* InitStrings.proto */
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
+
+
+/* Module declarations from 'cpython.buffer' */
+
+/* Module declarations from 'libc.string' */
+
+/* Module declarations from 'libc.stdio' */
+
+/* Module declarations from '__builtin__' */
+
+/* Module declarations from 'cpython.type' */
+static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
+
+/* Module declarations from 'cpython' */
+
+/* Module declarations from 'cpython.object' */
+
+/* Module declarations from 'cpython.ref' */
+
+/* Module declarations from 'libc.stdlib' */
+
+/* Module declarations from 'numpy' */
+
+/* Module declarations from 'numpy' */
+static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
+static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
+static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/
+
+/* Module declarations from 'pycocotools._mask' */
+static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_RLEs = 0;
+static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_Masks = 0;
+static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint8_t = { "uint8_t", NULL, sizeof(__pyx_t_5numpy_uint8_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint8_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint8_t), 0 };
+static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_double_t = { "double_t", NULL, sizeof(__pyx_t_5numpy_double_t), { 0 }, 0, 'R', 0, 0 };
+static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint32_t = { "uint32_t", NULL, sizeof(__pyx_t_5numpy_uint32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint32_t), 0 };
+#define __Pyx_MODULE_NAME "pycocotools._mask"
+int __pyx_module_is_main_pycocotools___mask = 0;
+
+/* Implementation of 'pycocotools._mask' */
+static PyObject *__pyx_builtin_range;
+static PyObject *__pyx_builtin_AttributeError;
+static PyObject *__pyx_builtin_enumerate;
+static PyObject *__pyx_builtin_ValueError;
+static PyObject *__pyx_builtin_RuntimeError;
+static PyObject *__pyx_builtin_ImportError;
+static const char __pyx_k_F[] = "F";
+static const char __pyx_k_N[] = "N";
+static const char __pyx_k_R[] = "R";
+static const char __pyx_k_a[] = "_a";
+static const char __pyx_k_h[] = "h";
+static const char __pyx_k_i[] = "i";
+static const char __pyx_k_j[] = "j";
+static const char __pyx_k_m[] = "m";
+static const char __pyx_k_n[] = "n";
+static const char __pyx_k_p[] = "p";
+static const char __pyx_k_w[] = "w";
+static const char __pyx_k_Rs[] = "Rs";
+static const char __pyx_k_bb[] = "bb";
+static const char __pyx_k_dt[] = "dt";
+static const char __pyx_k_gt[] = "gt";
+static const char __pyx_k_np[] = "np";
+static const char __pyx_k_a_2[] = "a";
+static const char __pyx_k_all[] = "all";
+static const char __pyx_k_iou[] = "_iou";
+static const char __pyx_k_len[] = "_len";
+static const char __pyx_k_obj[] = "obj";
+static const char __pyx_k_area[] = "area";
+static const char __pyx_k_bb_2[] = "_bb";
+static const char __pyx_k_cnts[] = "cnts";
+static const char __pyx_k_data[] = "data";
+static const char __pyx_k_main[] = "__main__";
+static const char __pyx_k_mask[] = "mask";
+static const char __pyx_k_objs[] = "objs";
+static const char __pyx_k_poly[] = "poly";
+static const char __pyx_k_size[] = "size";
+static const char __pyx_k_test[] = "__test__";
+static const char __pyx_k_array[] = "array";
+static const char __pyx_k_bbIou[] = "_bbIou";
+static const char __pyx_k_dtype[] = "dtype";
+static const char __pyx_k_iou_2[] = "iou";
+static const char __pyx_k_isbox[] = "isbox";
+static const char __pyx_k_isrle[] = "isrle";
+static const char __pyx_k_masks[] = "masks";
+static const char __pyx_k_merge[] = "merge";
+static const char __pyx_k_numpy[] = "numpy";
+static const char __pyx_k_order[] = "order";
+static const char __pyx_k_pyobj[] = "pyobj";
+static const char __pyx_k_range[] = "range";
+static const char __pyx_k_shape[] = "shape";
+static const char __pyx_k_uint8[] = "uint8";
+static const char __pyx_k_zeros[] = "zeros";
+static const char __pyx_k_astype[] = "astype";
+static const char __pyx_k_author[] = "__author__";
+static const char __pyx_k_counts[] = "counts";
+static const char __pyx_k_decode[] = "decode";
+static const char __pyx_k_double[] = "double";
+static const char __pyx_k_encode[] = "encode";
+static const char __pyx_k_frBbox[] = "frBbox";
+static const char __pyx_k_frPoly[] = "frPoly";
+static const char __pyx_k_import[] = "__import__";
+static const char __pyx_k_iouFun[] = "_iouFun";
+static const char __pyx_k_rleIou[] = "_rleIou";
+static const char __pyx_k_toBbox[] = "toBbox";
+static const char __pyx_k_ucRles[] = "ucRles";
+static const char __pyx_k_uint32[] = "uint32";
+static const char __pyx_k_iscrowd[] = "iscrowd";
+static const char __pyx_k_np_poly[] = "np_poly";
+static const char __pyx_k_preproc[] = "_preproc";
+static const char __pyx_k_reshape[] = "reshape";
+static const char __pyx_k_rleObjs[] = "rleObjs";
+static const char __pyx_k_tsungyi[] = "tsungyi";
+static const char __pyx_k_c_string[] = "c_string";
+static const char __pyx_k_frString[] = "_frString";
+static const char __pyx_k_toString[] = "_toString";
+static const char __pyx_k_enumerate[] = "enumerate";
+static const char __pyx_k_intersect[] = "intersect";
+static const char __pyx_k_py_string[] = "py_string";
+static const char __pyx_k_pyiscrowd[] = "pyiscrowd";
+static const char __pyx_k_ValueError[] = "ValueError";
+static const char __pyx_k_ImportError[] = "ImportError";
+static const char __pyx_k_frPyObjects[] = "frPyObjects";
+static const char __pyx_k_RuntimeError[] = "RuntimeError";
+static const char __pyx_k_AttributeError[] = "AttributeError";
+static const char __pyx_k_iou_locals__len[] = "iou.<locals>._len";
+static const char __pyx_k_frUncompressedRLE[] = "frUncompressedRLE";
+static const char __pyx_k_iou_locals__bbIou[] = "iou.<locals>._bbIou";
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+static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
+static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
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+static PyObject *__pyx_n_s_bbIou;
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+static PyObject *__pyx_n_s_encode;
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+static PyObject *__pyx_n_s_frPoly;
+static PyObject *__pyx_n_s_frPyObjects;
+static PyObject *__pyx_n_s_frString;
+static PyObject *__pyx_n_s_frUncompressedRLE;
+static PyObject *__pyx_n_s_gt;
+static PyObject *__pyx_n_s_h;
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+static PyObject *__pyx_n_s_iou_2;
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+static PyObject *__pyx_n_s_iou_locals__len;
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+static PyObject *__pyx_n_s_iou_locals__rleIou;
+static PyObject *__pyx_n_s_isbox;
+static PyObject *__pyx_n_s_iscrowd;
+static PyObject *__pyx_n_s_isrle;
+static PyObject *__pyx_n_s_j;
+static PyObject *__pyx_n_s_len;
+static PyObject *__pyx_kp_s_list_input_can_be_bounding_box_N;
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+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_mask;
+static PyObject *__pyx_n_s_masks;
+static PyObject *__pyx_n_s_merge;
+static PyObject *__pyx_n_s_n;
+static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
+static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
+static PyObject *__pyx_n_s_np;
+static PyObject *__pyx_n_s_np_poly;
+static PyObject *__pyx_n_s_numpy;
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+static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;
+static PyObject *__pyx_kp_s_numpy_ndarray_input_is_only_for;
+static PyObject *__pyx_n_s_obj;
+static PyObject *__pyx_n_s_objs;
+static PyObject *__pyx_n_s_order;
+static PyObject *__pyx_n_s_p;
+static PyObject *__pyx_n_s_poly;
+static PyObject *__pyx_n_s_preproc;
+static PyObject *__pyx_n_s_py_string;
+static PyObject *__pyx_n_s_pycocotools__mask;
+static PyObject *__pyx_n_s_pyiscrowd;
+static PyObject *__pyx_n_s_pyobj;
+static PyObject *__pyx_n_s_range;
+static PyObject *__pyx_n_s_reshape;
+static PyObject *__pyx_n_s_rleIou;
+static PyObject *__pyx_n_s_rleObjs;
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+static PyObject *__pyx_n_s_size;
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+static PyObject *__pyx_n_s_toBbox;
+static PyObject *__pyx_n_s_toString;
+static PyObject *__pyx_n_s_tsungyi;
+static PyObject *__pyx_n_s_ucRles;
+static PyObject *__pyx_n_s_uint32;
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+static void __pyx_pf_11pycocotools_5_mask_4RLEs_2__dealloc__(struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_4RLEs_4__getattr__(struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_self, PyObject *__pyx_v_key); /* proto */
+static int __pyx_pf_11pycocotools_5_mask_5Masks___cinit__(struct __pyx_obj_11pycocotools_5_mask_Masks *__pyx_v_self, PyObject *__pyx_v_h, PyObject *__pyx_v_w, PyObject *__pyx_v_n); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_5Masks_2__array__(struct __pyx_obj_11pycocotools_5_mask_Masks *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask__toString(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_Rs); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_2_frString(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_4encode(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_mask); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_6decode(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */
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+static PyObject *__pyx_pf_11pycocotools_5_mask_10area(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_3iou__preproc(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_objs); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_3iou_2_rleIou(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_dt, struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_gt, PyArrayObject *__pyx_v_iscrowd, siz __pyx_v_m, siz __pyx_v_n, PyArrayObject *__pyx_v__iou); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_3iou_4_bbIou(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_dt, PyArrayObject *__pyx_v_gt, PyArrayObject *__pyx_v_iscrowd, siz __pyx_v_m, siz __pyx_v_n, PyArrayObject *__pyx_v__iou); /* proto */
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+static PyObject *__pyx_pf_11pycocotools_5_mask_12iou(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dt, PyObject *__pyx_v_gt, PyObject *__pyx_v_pyiscrowd); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_14toBbox(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */
+static PyObject *__pyx_pf_11pycocotools_5_mask_16frBbox(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_bb, siz __pyx_v_h, siz __pyx_v_w); /* proto */
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+static PyObject *__pyx_tp_new_11pycocotools_5_mask_Masks(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
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+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    if ((**argname == key) || (
+                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
+                             && _PyString_Eq(**argname, key))) {
+                        goto arg_passed_twice;
+                    }
+                    argname++;
+                }
+            }
+        } else
+        #endif
+        if (likely(PyUnicode_Check(key))) {
+            while (*name) {
+                int cmp = (**name == key) ? 0 :
+                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
+                #endif
+                    PyUnicode_Compare(**name, key);
+                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                if (cmp == 0) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    int cmp = (**argname == key) ? 0 :
+                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
+                    #endif
+                        PyUnicode_Compare(**argname, key);
+                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                    if (cmp == 0) goto arg_passed_twice;
+                    argname++;
+                }
+            }
+        } else
+            goto invalid_keyword_type;
+        if (kwds2) {
+            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
+        } else {
+            goto invalid_keyword;
+        }
+    }
+    return 0;
+arg_passed_twice:
+    __Pyx_RaiseDoubleKeywordsError(function_name, key);
+    goto bad;
+invalid_keyword_type:
+    PyErr_Format(PyExc_TypeError,
+        "%.200s() keywords must be strings", function_name);
+    goto bad;
+invalid_keyword:
+    PyErr_Format(PyExc_TypeError,
+    #if PY_MAJOR_VERSION < 3
+        "%.200s() got an unexpected keyword argument '%.200s'",
+        function_name, PyString_AsString(key));
+    #else
+        "%s() got an unexpected keyword argument '%U'",
+        function_name, key);
+    #endif
+bad:
+    return -1;
+}
+
+/* RaiseArgTupleInvalid */
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
+/* BytesEquals */
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+    if (s1 == s2) {
+        return (equals == Py_EQ);
+    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
+        const char *ps1, *ps2;
+        Py_ssize_t length = PyBytes_GET_SIZE(s1);
+        if (length != PyBytes_GET_SIZE(s2))
+            return (equals == Py_NE);
+        ps1 = PyBytes_AS_STRING(s1);
+        ps2 = PyBytes_AS_STRING(s2);
+        if (ps1[0] != ps2[0]) {
+            return (equals == Py_NE);
+        } else if (length == 1) {
+            return (equals == Py_EQ);
+        } else {
+            int result = memcmp(ps1, ps2, (size_t)length);
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
+        return (equals == Py_NE);
+    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
+        return (equals == Py_NE);
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+#endif
+}
+
+/* UnicodeEquals */
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+#if PY_MAJOR_VERSION < 3
+    PyObject* owned_ref = NULL;
+#endif
+    int s1_is_unicode, s2_is_unicode;
+    if (s1 == s2) {
+        goto return_eq;
+    }
+    s1_is_unicode = PyUnicode_CheckExact(s1);
+    s2_is_unicode = PyUnicode_CheckExact(s2);
+#if PY_MAJOR_VERSION < 3
+    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
+        owned_ref = PyUnicode_FromObject(s2);
+        if (unlikely(!owned_ref))
+            return -1;
+        s2 = owned_ref;
+        s2_is_unicode = 1;
+    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
+        owned_ref = PyUnicode_FromObject(s1);
+        if (unlikely(!owned_ref))
+            return -1;
+        s1 = owned_ref;
+        s1_is_unicode = 1;
+    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
+        return __Pyx_PyBytes_Equals(s1, s2, equals);
+    }
+#endif
+    if (s1_is_unicode & s2_is_unicode) {
+        Py_ssize_t length;
+        int kind;
+        void *data1, *data2;
+        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
+            return -1;
+        length = __Pyx_PyUnicode_GET_LENGTH(s1);
+        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
+            goto return_ne;
+        }
+        kind = __Pyx_PyUnicode_KIND(s1);
+        if (kind != __Pyx_PyUnicode_KIND(s2)) {
+            goto return_ne;
+        }
+        data1 = __Pyx_PyUnicode_DATA(s1);
+        data2 = __Pyx_PyUnicode_DATA(s2);
+        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
+            goto return_ne;
+        } else if (length == 1) {
+            goto return_eq;
+        } else {
+            int result = memcmp(data1, data2, (size_t)(length * kind));
+            #if PY_MAJOR_VERSION < 3
+            Py_XDECREF(owned_ref);
+            #endif
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & s2_is_unicode) {
+        goto return_ne;
+    } else if ((s2 == Py_None) & s1_is_unicode) {
+        goto return_ne;
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+return_eq:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_EQ);
+return_ne:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_NE);
+#endif
+}
+
+/* PyObjectCall */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    PyObject *result;
+    ternaryfunc call = func->ob_type->tp_call;
+    if (unlikely(!call))
+        return PyObject_Call(func, arg, kw);
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+    result = (*call)(func, arg, kw);
+    Py_LeaveRecursiveCall();
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+/* PyErrFetchRestore */
+#if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    tmp_type = tstate->curexc_type;
+    tmp_value = tstate->curexc_value;
+    tmp_tb = tstate->curexc_traceback;
+    tstate->curexc_type = type;
+    tstate->curexc_value = value;
+    tstate->curexc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    *type = tstate->curexc_type;
+    *value = tstate->curexc_value;
+    *tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+}
+#endif
+
+/* RaiseException */
+#if PY_MAJOR_VERSION < 3
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+                        CYTHON_UNUSED PyObject *cause) {
+    __Pyx_PyThreadState_declare
+    Py_XINCREF(type);
+    if (!value || value == Py_None)
+        value = NULL;
+    else
+        Py_INCREF(value);
+    if (!tb || tb == Py_None)
+        tb = NULL;
+    else {
+        Py_INCREF(tb);
+        if (!PyTraceBack_Check(tb)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: arg 3 must be a traceback or None");
+            goto raise_error;
+        }
+    }
+    if (PyType_Check(type)) {
+#if CYTHON_COMPILING_IN_PYPY
+        if (!value) {
+            Py_INCREF(Py_None);
+            value = Py_None;
+        }
+#endif
+        PyErr_NormalizeException(&type, &value, &tb);
+    } else {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto raise_error;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(type);
+        Py_INCREF(type);
+        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: exception class must be a subclass of BaseException");
+            goto raise_error;
+        }
+    }
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrRestore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+#else
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
+    PyObject* owned_instance = NULL;
+    if (tb == Py_None) {
+        tb = 0;
+    } else if (tb && !PyTraceBack_Check(tb)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: arg 3 must be a traceback or None");
+        goto bad;
+    }
+    if (value == Py_None)
+        value = 0;
+    if (PyExceptionInstance_Check(type)) {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto bad;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(value);
+    } else if (PyExceptionClass_Check(type)) {
+        PyObject *instance_class = NULL;
+        if (value && PyExceptionInstance_Check(value)) {
+            instance_class = (PyObject*) Py_TYPE(value);
+            if (instance_class != type) {
+                int is_subclass = PyObject_IsSubclass(instance_class, type);
+                if (!is_subclass) {
+                    instance_class = NULL;
+                } else if (unlikely(is_subclass == -1)) {
+                    goto bad;
+                } else {
+                    type = instance_class;
+                }
+            }
+        }
+        if (!instance_class) {
+            PyObject *args;
+            if (!value)
+                args = PyTuple_New(0);
+            else if (PyTuple_Check(value)) {
+                Py_INCREF(value);
+                args = value;
+            } else
+                args = PyTuple_Pack(1, value);
+            if (!args)
+                goto bad;
+            owned_instance = PyObject_Call(type, args, NULL);
+            Py_DECREF(args);
+            if (!owned_instance)
+                goto bad;
+            value = owned_instance;
+            if (!PyExceptionInstance_Check(value)) {
+                PyErr_Format(PyExc_TypeError,
+                             "calling %R should have returned an instance of "
+                             "BaseException, not %R",
+                             type, Py_TYPE(value));
+                goto bad;
+            }
+        }
+    } else {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: exception class must be a subclass of BaseException");
+        goto bad;
+    }
+#if PY_VERSION_HEX >= 0x03030000
+    if (cause) {
+#else
+    if (cause && cause != Py_None) {
+#endif
+        PyObject *fixed_cause;
+        if (cause == Py_None) {
+            fixed_cause = NULL;
+        } else if (PyExceptionClass_Check(cause)) {
+            fixed_cause = PyObject_CallObject(cause, NULL);
+            if (fixed_cause == NULL)
+                goto bad;
+        } else if (PyExceptionInstance_Check(cause)) {
+            fixed_cause = cause;
+            Py_INCREF(fixed_cause);
+        } else {
+            PyErr_SetString(PyExc_TypeError,
+                            "exception causes must derive from "
+                            "BaseException");
+            goto bad;
+        }
+        PyException_SetCause(value, fixed_cause);
+    }
+    PyErr_SetObject(type, value);
+    if (tb) {
+#if CYTHON_COMPILING_IN_PYPY
+        PyObject *tmp_type, *tmp_value, *tmp_tb;
+        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
+        Py_INCREF(tb);
+        PyErr_Restore(tmp_type, tmp_value, tb);
+        Py_XDECREF(tmp_tb);
+#else
+        PyThreadState *tstate = PyThreadState_GET();
+        PyObject* tmp_tb = tstate->curexc_traceback;
+        if (tb != tmp_tb) {
+            Py_INCREF(tb);
+            tstate->curexc_traceback = tb;
+            Py_XDECREF(tmp_tb);
+        }
+#endif
+    }
+bad:
+    Py_XDECREF(owned_instance);
+    return;
+}
+#endif
+
+/* ExtTypeTest */
+  static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (likely(PyObject_TypeCheck(obj, type)))
+        return 1;
+    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+                 Py_TYPE(obj)->tp_name, type->tp_name);
+    return 0;
+}
+
+/* ArgTypeTest */
+  static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
+    PyErr_Format(PyExc_TypeError,
+        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+        name, type->tp_name, Py_TYPE(obj)->tp_name);
+}
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+    const char *name, int exact)
+{
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (none_allowed && obj == Py_None) return 1;
+    else if (exact) {
+        if (likely(Py_TYPE(obj) == type)) return 1;
+        #if PY_MAJOR_VERSION == 2
+        else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+        #endif
+    }
+    else {
+        if (likely(PyObject_TypeCheck(obj, type))) return 1;
+    }
+    __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
+    return 0;
+}
+
+/* PyIntBinop */
+  #if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
+    #if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_CheckExact(op1))) {
+        const long b = intval;
+        long x;
+        long a = PyInt_AS_LONG(op1);
+            x = (long)((unsigned long)a + b);
+            if (likely((x^a) >= 0 || (x^b) >= 0))
+                return PyInt_FromLong(x);
+            return PyLong_Type.tp_as_number->nb_add(op1, op2);
+    }
+    #endif
+    #if CYTHON_USE_PYLONG_INTERNALS
+    if (likely(PyLong_CheckExact(op1))) {
+        const long b = intval;
+        long a, x;
+#ifdef HAVE_LONG_LONG
+        const PY_LONG_LONG llb = intval;
+        PY_LONG_LONG lla, llx;
+#endif
+        const digit* digits = ((PyLongObject*)op1)->ob_digit;
+        const Py_ssize_t size = Py_SIZE(op1);
+        if (likely(__Pyx_sst_abs(size) <= 1)) {
+            a = likely(size) ? digits[0] : 0;
+            if (size == -1) a = -a;
+        } else {
+            switch (size) {
+                case -2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                case 2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                case -3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                case 3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                case -4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                case 4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
+            }
+        }
+                x = a + b;
+            return PyLong_FromLong(x);
+#ifdef HAVE_LONG_LONG
+        long_long:
+                llx = lla + llb;
+            return PyLong_FromLongLong(llx);
+#endif
+        
+        
+    }
+    #endif
+    if (PyFloat_CheckExact(op1)) {
+        const long b = intval;
+        double a = PyFloat_AS_DOUBLE(op1);
+            double result;
+            PyFPE_START_PROTECT("add", return NULL)
+            result = ((double)a) + (double)b;
+            PyFPE_END_PROTECT(result)
+            return PyFloat_FromDouble(result);
+    }
+    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
+}
+#endif
+
+/* GetItemInt */
+  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+    PyObject *r;
+    if (!j) return NULL;
+    r = PyObject_GetItem(o, j);
+    Py_DECREF(j);
+    return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
+        PyObject *r = PyList_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
+        PyObject *r = PyTuple_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
+                                                     CYTHON_NCP_UNUSED int wraparound,
+                                                     CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
+    if (is_list || PyList_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    }
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    } else {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+                Py_ssize_t l = m->sq_length(o);
+                if (likely(l >= 0)) {
+                    i += l;
+                } else {
+                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
+                        return NULL;
+                    PyErr_Clear();
+                }
+            }
+            return m->sq_item(o, i);
+        }
+    }
+#else
+    if (is_list || PySequence_Check(o)) {
+        return PySequence_GetItem(o, i);
+    }
+#endif
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+/* BufferFormatCheck */
+  static CYTHON_INLINE int __Pyx_IsLittleEndian(void) {
+  unsigned int n = 1;
+  return *(unsigned char*)(&n) != 0;
+}
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type) {
+  stack[0].field = &ctx->root;
+  stack[0].parent_offset = 0;
+  ctx->root.type = type;
+  ctx->root.name = "buffer dtype";
+  ctx->root.offset = 0;
+  ctx->head = stack;
+  ctx->head->field = &ctx->root;
+  ctx->fmt_offset = 0;
+  ctx->head->parent_offset = 0;
+  ctx->new_packmode = '@';
+  ctx->enc_packmode = '@';
+  ctx->new_count = 1;
+  ctx->enc_count = 0;
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  ctx->is_valid_array = 0;
+  ctx->struct_alignment = 0;
+  while (type->typegroup == 'S') {
+    ++ctx->head;
+    ctx->head->field = type->fields;
+    ctx->head->parent_offset = 0;
+    type = type->fields->type;
+  }
+}
+static int __Pyx_BufFmt_ParseNumber(const char** ts) {
+    int count;
+    const char* t = *ts;
+    if (*t < '0' || *t > '9') {
+      return -1;
+    } else {
+        count = *t++ - '0';
+        while (*t >= '0' && *t < '9') {
+            count *= 10;
+            count += *t++ - '0';
+        }
+    }
+    *ts = t;
+    return count;
+}
+static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
+    int number = __Pyx_BufFmt_ParseNumber(ts);
+    if (number == -1)
+        PyErr_Format(PyExc_ValueError,\
+                     "Does not understand character buffer dtype format string ('%c')", **ts);
+    return number;
+}
+static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
+  PyErr_Format(PyExc_ValueError,
+               "Unexpected format string character: '%c'", ch);
+}
+static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': return "'char'";
+    case 'b': return "'signed char'";
+    case 'B': return "'unsigned char'";
+    case 'h': return "'short'";
+    case 'H': return "'unsigned short'";
+    case 'i': return "'int'";
+    case 'I': return "'unsigned int'";
+    case 'l': return "'long'";
+    case 'L': return "'unsigned long'";
+    case 'q': return "'long long'";
+    case 'Q': return "'unsigned long long'";
+    case 'f': return (is_complex ? "'complex float'" : "'float'");
+    case 'd': return (is_complex ? "'complex double'" : "'double'");
+    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
+    case 'T': return "a struct";
+    case 'O': return "Python object";
+    case 'P': return "a pointer";
+    case 's': case 'p': return "a string";
+    case 0: return "end";
+    default: return "unparseable format string";
+  }
+}
+static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return 2;
+    case 'i': case 'I': case 'l': case 'L': return 4;
+    case 'q': case 'Q': return 8;
+    case 'f': return (is_complex ? 8 : 4);
+    case 'd': return (is_complex ? 16 : 8);
+    case 'g': {
+      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
+      return 0;
+    }
+    case 'O': case 'P': return sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(short);
+    case 'i': case 'I': return sizeof(int);
+    case 'l': case 'L': return sizeof(long);
+    #ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
+    #endif
+    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
+    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
+    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
+    case 'O': case 'P': return sizeof(void*);
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+typedef struct { char c; short x; } __Pyx_st_short;
+typedef struct { char c; int x; } __Pyx_st_int;
+typedef struct { char c; long x; } __Pyx_st_long;
+typedef struct { char c; float x; } __Pyx_st_float;
+typedef struct { char c; double x; } __Pyx_st_double;
+typedef struct { char c; long double x; } __Pyx_st_longdouble;
+typedef struct { char c; void *x; } __Pyx_st_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+/* These are for computing the padding at the end of the struct to align
+   on the first member of the struct. This will probably the same as above,
+   but we don't have any guarantees.
+ */
+typedef struct { short x; char c; } __Pyx_pad_short;
+typedef struct { int x; char c; } __Pyx_pad_int;
+typedef struct { long x; char c; } __Pyx_pad_long;
+typedef struct { float x; char c; } __Pyx_pad_float;
+typedef struct { double x; char c; } __Pyx_pad_double;
+typedef struct { long double x; char c; } __Pyx_pad_longdouble;
+typedef struct { void *x; char c; } __Pyx_pad_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
+  switch (ch) {
+    case 'c':
+        return 'H';
+    case 'b': case 'h': case 'i':
+    case 'l': case 'q': case 's': case 'p':
+        return 'I';
+    case 'B': case 'H': case 'I': case 'L': case 'Q':
+        return 'U';
+    case 'f': case 'd': case 'g':
+        return (is_complex ? 'C' : 'R');
+    case 'O':
+        return 'O';
+    case 'P':
+        return 'P';
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
+  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
+    const char* expected;
+    const char* quote;
+    if (ctx->head == NULL) {
+      expected = "end";
+      quote = "";
+    } else {
+      expected = ctx->head->field->type->name;
+      quote = "'";
+    }
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected %s%s%s but got %s",
+                 quote, expected, quote,
+                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
+  } else {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_StructField* parent = (ctx->head - 1)->field;
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
+                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
+                 parent->type->name, field->name);
+  }
+}
+static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
+  char group;
+  size_t size, offset, arraysize = 1;
+  if (ctx->enc_type == 0) return 0;
+  if (ctx->head->field->type->arraysize[0]) {
+    int i, ndim = 0;
+    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
+        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
+        ndim = 1;
+        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
+            PyErr_Format(PyExc_ValueError,
+                         "Expected a dimension of size %zu, got %zu",
+                         ctx->head->field->type->arraysize[0], ctx->enc_count);
+            return -1;
+        }
+    }
+    if (!ctx->is_valid_array) {
+      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
+                   ctx->head->field->type->ndim, ndim);
+      return -1;
+    }
+    for (i = 0; i < ctx->head->field->type->ndim; i++) {
+      arraysize *= ctx->head->field->type->arraysize[i];
+    }
+    ctx->is_valid_array = 0;
+    ctx->enc_count = 1;
+  }
+  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
+  do {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_TypeInfo* type = field->type;
+    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
+      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
+    } else {
+      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    }
+    if (ctx->enc_packmode == '@') {
+      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
+      size_t align_mod_offset;
+      if (align_at == 0) return -1;
+      align_mod_offset = ctx->fmt_offset % align_at;
+      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
+      if (ctx->struct_alignment == 0)
+          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
+                                                                 ctx->is_complex);
+    }
+    if (type->size != size || type->typegroup != group) {
+      if (type->typegroup == 'C' && type->fields != NULL) {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        ++ctx->head;
+        ctx->head->field = type->fields;
+        ctx->head->parent_offset = parent_offset;
+        continue;
+      }
+      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
+      } else {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+      }
+    }
+    offset = ctx->head->parent_offset + field->offset;
+    if (ctx->fmt_offset != offset) {
+      PyErr_Format(PyExc_ValueError,
+                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
+                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
+      return -1;
+    }
+    ctx->fmt_offset += size;
+    if (arraysize)
+      ctx->fmt_offset += (arraysize - 1) * size;
+    --ctx->enc_count;
+    while (1) {
+      if (field == &ctx->root) {
+        ctx->head = NULL;
+        if (ctx->enc_count != 0) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+        }
+        break;
+      }
+      ctx->head->field = ++field;
+      if (field->type == NULL) {
+        --ctx->head;
+        field = ctx->head->field;
+        continue;
+      } else if (field->type->typegroup == 'S') {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        if (field->type->fields->type == NULL) continue;
+        field = field->type->fields;
+        ++ctx->head;
+        ctx->head->field = field;
+        ctx->head->parent_offset = parent_offset;
+        break;
+      } else {
+        break;
+      }
+    }
+  } while (ctx->enc_count);
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  return 0;
+}
+static CYTHON_INLINE PyObject *
+__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
+{
+    const char *ts = *tsp;
+    int i = 0, number;
+    int ndim = ctx->head->field->type->ndim;
+;
+    ++ts;
+    if (ctx->new_count != 1) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Cannot handle repeated arrays in format string");
+        return NULL;
+    }
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    while (*ts && *ts != ')') {
+        switch (*ts) {
+            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
+            default:  break;
+        }
+        number = __Pyx_BufFmt_ExpectNumber(&ts);
+        if (number == -1) return NULL;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
+            return PyErr_Format(PyExc_ValueError,
+                        "Expected a dimension of size %zu, got %d",
+                        ctx->head->field->type->arraysize[i], number);
+        if (*ts != ',' && *ts != ')')
+            return PyErr_Format(PyExc_ValueError,
+                                "Expected a comma in format string, got '%c'", *ts);
+        if (*ts == ',') ts++;
+        i++;
+    }
+    if (i != ndim)
+        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+                            ctx->head->field->type->ndim, i);
+    if (!*ts) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Unexpected end of format string, expected ')'");
+        return NULL;
+    }
+    ctx->is_valid_array = 1;
+    ctx->new_count = 1;
+    *tsp = ++ts;
+    return Py_None;
+}
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
+  int got_Z = 0;
+  while (1) {
+    switch(*ts) {
+      case 0:
+        if (ctx->enc_type != 0 && ctx->head == NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        if (ctx->head != NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        return ts;
+      case ' ':
+      case '\r':
+      case '\n':
+        ++ts;
+        break;
+      case '<':
+        if (!__Pyx_IsLittleEndian()) {
+          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '>':
+      case '!':
+        if (__Pyx_IsLittleEndian()) {
+          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '=':
+      case '@':
+      case '^':
+        ctx->new_packmode = *ts++;
+        break;
+      case 'T':
+        {
+          const char* ts_after_sub;
+          size_t i, struct_count = ctx->new_count;
+          size_t struct_alignment = ctx->struct_alignment;
+          ctx->new_count = 1;
+          ++ts;
+          if (*ts != '{') {
+            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
+            return NULL;
+          }
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          ctx->enc_count = 0;
+          ctx->struct_alignment = 0;
+          ++ts;
+          ts_after_sub = ts;
+          for (i = 0; i != struct_count; ++i) {
+            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
+            if (!ts_after_sub) return NULL;
+          }
+          ts = ts_after_sub;
+          if (struct_alignment) ctx->struct_alignment = struct_alignment;
+        }
+        break;
+      case '}':
+        {
+          size_t alignment = ctx->struct_alignment;
+          ++ts;
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          if (alignment && ctx->fmt_offset % alignment) {
+            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
+          }
+        }
+        return ts;
+      case 'x':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->fmt_offset += ctx->new_count;
+        ctx->new_count = 1;
+        ctx->enc_count = 0;
+        ctx->enc_type = 0;
+        ctx->enc_packmode = ctx->new_packmode;
+        ++ts;
+        break;
+      case 'Z':
+        got_Z = 1;
+        ++ts;
+        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
+          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
+          return NULL;
+        }
+      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
+      case 'l': case 'L': case 'q': case 'Q':
+      case 'f': case 'd': case 'g':
+      case 'O': case 'p':
+        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
+            ctx->enc_packmode == ctx->new_packmode) {
+          ctx->enc_count += ctx->new_count;
+          ctx->new_count = 1;
+          got_Z = 0;
+          ++ts;
+          break;
+        }
+      case 's':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->enc_count = ctx->new_count;
+        ctx->enc_packmode = ctx->new_packmode;
+        ctx->enc_type = *ts;
+        ctx->is_complex = got_Z;
+        ++ts;
+        ctx->new_count = 1;
+        got_Z = 0;
+        break;
+      case ':':
+        ++ts;
+        while(*ts != ':') ++ts;
+        ++ts;
+        break;
+      case '(':
+        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        break;
+      default:
+        {
+          int number = __Pyx_BufFmt_ExpectNumber(&ts);
+          if (number == -1) return NULL;
+          ctx->new_count = (size_t)number;
+        }
+    }
+  }
+}
+static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
+  buf->buf = NULL;
+  buf->obj = NULL;
+  buf->strides = __Pyx_zeros;
+  buf->shape = __Pyx_zeros;
+  buf->suboffsets = __Pyx_minusones;
+}
+static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
+        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
+        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
+{
+  if (obj == Py_None || obj == NULL) {
+    __Pyx_ZeroBuffer(buf);
+    return 0;
+  }
+  buf->buf = NULL;
+  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
+  if (buf->ndim != nd) {
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer has wrong number of dimensions (expected %d, got %d)",
+                 nd, buf->ndim);
+    goto fail;
+  }
+  if (!cast) {
+    __Pyx_BufFmt_Context ctx;
+    __Pyx_BufFmt_Init(&ctx, stack, dtype);
+    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
+  }
+  if ((unsigned)buf->itemsize != dtype->size) {
+    PyErr_Format(PyExc_ValueError,
+      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
+      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
+      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
+    goto fail;
+  }
+  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
+  return 0;
+fail:;
+  __Pyx_ZeroBuffer(buf);
+  return -1;
+}
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
+  if (info->buf == NULL) return;
+  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
+  __Pyx_ReleaseBuffer(info);
+}
+
+/* GetModuleGlobalName */
+    static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
+    PyObject *result;
+#if !CYTHON_AVOID_BORROWED_REFS
+    result = PyDict_GetItem(__pyx_d, name);
+    if (likely(result)) {
+        Py_INCREF(result);
+    } else {
+#else
+    result = PyObject_GetItem(__pyx_d, name);
+    if (!result) {
+        PyErr_Clear();
+#endif
+        result = __Pyx_GetBuiltinName(name);
+    }
+    return result;
+}
+
+/* PyCFunctionFastCall */
+      #if CYTHON_FAST_PYCCALL
+static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
+    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
+    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
+    PyObject *self = PyCFunction_GET_SELF(func);
+    assert(PyCFunction_Check(func));
+    assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)));
+    assert(nargs >= 0);
+    assert(nargs == 0 || args != NULL);
+    /* _PyCFunction_FastCallDict() must not be called with an exception set,
+       because it may clear it (directly or indirectly) and so the
+       caller loses its exception */
+    assert(!PyErr_Occurred());
+    return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL);
+}
+#endif  // CYTHON_FAST_PYCCALL
+
+/* PyFunctionFastCall */
+      #if CYTHON_FAST_PYCALL
+#include "frameobject.h"
+static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
+                                               PyObject *globals) {
+    PyFrameObject *f;
+    PyThreadState *tstate = PyThreadState_GET();
+    PyObject **fastlocals;
+    Py_ssize_t i;
+    PyObject *result;
+    assert(globals != NULL);
+    /* XXX Perhaps we should create a specialized
+       PyFrame_New() that doesn't take locals, but does
+       take builtins without sanity checking them.
+       */
+    assert(tstate != NULL);
+    f = PyFrame_New(tstate, co, globals, NULL);
+    if (f == NULL) {
+        return NULL;
+    }
+    fastlocals = f->f_localsplus;
+    for (i = 0; i < na; i++) {
+        Py_INCREF(*args);
+        fastlocals[i] = *args++;
+    }
+    result = PyEval_EvalFrameEx(f,0);
+    ++tstate->recursion_depth;
+    Py_DECREF(f);
+    --tstate->recursion_depth;
+    return result;
+}
+#if 1 || PY_VERSION_HEX < 0x030600B1
+static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {
+    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
+    PyObject *globals = PyFunction_GET_GLOBALS(func);
+    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
+    PyObject *closure;
+#if PY_MAJOR_VERSION >= 3
+    PyObject *kwdefs;
+#endif
+    PyObject *kwtuple, **k;
+    PyObject **d;
+    Py_ssize_t nd;
+    Py_ssize_t nk;
+    PyObject *result;
+    assert(kwargs == NULL || PyDict_Check(kwargs));
+    nk = kwargs ? PyDict_Size(kwargs) : 0;
+    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
+        return NULL;
+    }
+    if (
+#if PY_MAJOR_VERSION >= 3
+            co->co_kwonlyargcount == 0 &&
+#endif
+            likely(kwargs == NULL || nk == 0) &&
+            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
+        if (argdefs == NULL && co->co_argcount == nargs) {
+            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
+            goto done;
+        }
+        else if (nargs == 0 && argdefs != NULL
+                 && co->co_argcount == Py_SIZE(argdefs)) {
+            /* function called with no arguments, but all parameters have
+               a default value: use default values as arguments .*/
+            args = &PyTuple_GET_ITEM(argdefs, 0);
+            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
+            goto done;
+        }
+    }
+    if (kwargs != NULL) {
+        Py_ssize_t pos, i;
+        kwtuple = PyTuple_New(2 * nk);
+        if (kwtuple == NULL) {
+            result = NULL;
+            goto done;
+        }
+        k = &PyTuple_GET_ITEM(kwtuple, 0);
+        pos = i = 0;
+        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
+            Py_INCREF(k[i]);
+            Py_INCREF(k[i+1]);
+            i += 2;
+        }
+        nk = i / 2;
+    }
+    else {
+        kwtuple = NULL;
+        k = NULL;
+    }
+    closure = PyFunction_GET_CLOSURE(func);
+#if PY_MAJOR_VERSION >= 3
+    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
+#endif
+    if (argdefs != NULL) {
+        d = &PyTuple_GET_ITEM(argdefs, 0);
+        nd = Py_SIZE(argdefs);
+    }
+    else {
+        d = NULL;
+        nd = 0;
+    }
+#if PY_MAJOR_VERSION >= 3
+    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
+                               args, nargs,
+                               k, (int)nk,
+                               d, (int)nd, kwdefs, closure);
+#else
+    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
+                               args, nargs,
+                               k, (int)nk,
+                               d, (int)nd, closure);
+#endif
+    Py_XDECREF(kwtuple);
+done:
+    Py_LeaveRecursiveCall();
+    return result;
+}
+#endif  // CPython < 3.6
+#endif  // CYTHON_FAST_PYCALL
+
+/* PyObjectCallMethO */
+      #if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
+    PyObject *self, *result;
+    PyCFunction cfunc;
+    cfunc = PyCFunction_GET_FUNCTION(func);
+    self = PyCFunction_GET_SELF(func);
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+    result = cfunc(self, arg);
+    Py_LeaveRecursiveCall();
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+/* PyObjectCallOneArg */
+      #if CYTHON_COMPILING_IN_CPYTHON
+static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+    PyObject *result;
+    PyObject *args = PyTuple_New(1);
+    if (unlikely(!args)) return NULL;
+    Py_INCREF(arg);
+    PyTuple_SET_ITEM(args, 0, arg);
+    result = __Pyx_PyObject_Call(func, args, NULL);
+    Py_DECREF(args);
+    return result;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+#if CYTHON_FAST_PYCALL
+    if (PyFunction_Check(func)) {
+        return __Pyx_PyFunction_FastCall(func, &arg, 1);
+    }
+#endif
+#ifdef __Pyx_CyFunction_USED
+    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
+#else
+    if (likely(PyCFunction_Check(func))) {
+#endif
+        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
+            return __Pyx_PyObject_CallMethO(func, arg);
+#if CYTHON_FAST_PYCCALL
+        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
+            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
+#endif
+        }
+    }
+    return __Pyx__PyObject_CallOneArg(func, arg);
+}
+#else
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+    PyObject *result;
+    PyObject *args = PyTuple_Pack(1, arg);
+    if (unlikely(!args)) return NULL;
+    result = __Pyx_PyObject_Call(func, args, NULL);
+    Py_DECREF(args);
+    return result;
+}
+#endif
+
+/* PyIntBinop */
+        #if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
+    if (op1 == op2) {
+        Py_RETURN_TRUE;
+    }
+    #if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_CheckExact(op1))) {
+        const long b = intval;
+        long a = PyInt_AS_LONG(op1);
+        if (a == b) {
+            Py_RETURN_TRUE;
+        } else {
+            Py_RETURN_FALSE;
+        }
+    }
+    #endif
+    #if CYTHON_USE_PYLONG_INTERNALS
+    if (likely(PyLong_CheckExact(op1))) {
+        const long b = intval;
+        long a;
+        const digit* digits = ((PyLongObject*)op1)->ob_digit;
+        const Py_ssize_t size = Py_SIZE(op1);
+        if (likely(__Pyx_sst_abs(size) <= 1)) {
+            a = likely(size) ? digits[0] : 0;
+            if (size == -1) a = -a;
+        } else {
+            switch (size) {
+                case -2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                case 2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                case -3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                case 3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                case -4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                case 4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+                    }
+                #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
+                default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ);
+                #else
+                default: Py_RETURN_FALSE;
+                #endif
+            }
+        }
+            if (a == b) {
+                Py_RETURN_TRUE;
+            } else {
+                Py_RETURN_FALSE;
+            }
+    }
+    #endif
+    if (PyFloat_CheckExact(op1)) {
+        const long b = intval;
+        double a = PyFloat_AS_DOUBLE(op1);
+            if ((double)a == (double)b) {
+                Py_RETURN_TRUE;
+            } else {
+                Py_RETURN_FALSE;
+            }
+    }
+    return PyObject_RichCompare(op1, op2, Py_EQ);
+}
+#endif
+
+/* FetchCommonType */
+        static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
+    PyObject* fake_module;
+    PyTypeObject* cached_type = NULL;
+    fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
+    if (!fake_module) return NULL;
+    Py_INCREF(fake_module);
+    cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
+    if (cached_type) {
+        if (!PyType_Check((PyObject*)cached_type)) {
+            PyErr_Format(PyExc_TypeError,
+                "Shared Cython type %.200s is not a type object",
+                type->tp_name);
+            goto bad;
+        }
+        if (cached_type->tp_basicsize != type->tp_basicsize) {
+            PyErr_Format(PyExc_TypeError,
+                "Shared Cython type %.200s has the wrong size, try recompiling",
+                type->tp_name);
+            goto bad;
+        }
+    } else {
+        if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
+        PyErr_Clear();
+        if (PyType_Ready(type) < 0) goto bad;
+        if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
+            goto bad;
+        Py_INCREF(type);
+        cached_type = type;
+    }
+done:
+    Py_DECREF(fake_module);
+    return cached_type;
+bad:
+    Py_XDECREF(cached_type);
+    cached_type = NULL;
+    goto done;
+}
+
+/* CythonFunction */
+        static PyObject *
+__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
+{
+    if (unlikely(op->func_doc == NULL)) {
+        if (op->func.m_ml->ml_doc) {
+#if PY_MAJOR_VERSION >= 3
+            op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc);
+#else
+            op->func_doc = PyString_FromString(op->func.m_ml->ml_doc);
+#endif
+            if (unlikely(op->func_doc == NULL))
+                return NULL;
+        } else {
+            Py_INCREF(Py_None);
+            return Py_None;
+        }
+    }
+    Py_INCREF(op->func_doc);
+    return op->func_doc;
+}
+static int
+__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value)
+{
+    PyObject *tmp = op->func_doc;
+    if (value == NULL) {
+        value = Py_None;
+    }
+    Py_INCREF(value);
+    op->func_doc = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op)
+{
+    if (unlikely(op->func_name == NULL)) {
+#if PY_MAJOR_VERSION >= 3
+        op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name);
+#else
+        op->func_name = PyString_InternFromString(op->func.m_ml->ml_name);
+#endif
+        if (unlikely(op->func_name == NULL))
+            return NULL;
+    }
+    Py_INCREF(op->func_name);
+    return op->func_name;
+}
+static int
+__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value)
+{
+    PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+    if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+    if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+        PyErr_SetString(PyExc_TypeError,
+                        "__name__ must be set to a string object");
+        return -1;
+    }
+    tmp = op->func_name;
+    Py_INCREF(value);
+    op->func_name = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op)
+{
+    Py_INCREF(op->func_qualname);
+    return op->func_qualname;
+}
+static int
+__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value)
+{
+    PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+    if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+    if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+        PyErr_SetString(PyExc_TypeError,
+                        "__qualname__ must be set to a string object");
+        return -1;
+    }
+    tmp = op->func_qualname;
+    Py_INCREF(value);
+    op->func_qualname = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure)
+{
+    PyObject *self;
+    self = m->func_closure;
+    if (self == NULL)
+        self = Py_None;
+    Py_INCREF(self);
+    return self;
+}
+static PyObject *
+__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op)
+{
+    if (unlikely(op->func_dict == NULL)) {
+        op->func_dict = PyDict_New();
+        if (unlikely(op->func_dict == NULL))
+            return NULL;
+    }
+    Py_INCREF(op->func_dict);
+    return op->func_dict;
+}
+static int
+__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value)
+{
+    PyObject *tmp;
+    if (unlikely(value == NULL)) {
+        PyErr_SetString(PyExc_TypeError,
+               "function's dictionary may not be deleted");
+        return -1;
+    }
+    if (unlikely(!PyDict_Check(value))) {
+        PyErr_SetString(PyExc_TypeError,
+               "setting function's dictionary to a non-dict");
+        return -1;
+    }
+    tmp = op->func_dict;
+    Py_INCREF(value);
+    op->func_dict = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op)
+{
+    Py_INCREF(op->func_globals);
+    return op->func_globals;
+}
+static PyObject *
+__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op)
+{
+    Py_INCREF(Py_None);
+    return Py_None;
+}
+static PyObject *
+__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op)
+{
+    PyObject* result = (op->func_code) ? op->func_code : Py_None;
+    Py_INCREF(result);
+    return result;
+}
+static int
+__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
+    int result = 0;
+    PyObject *res = op->defaults_getter((PyObject *) op);
+    if (unlikely(!res))
+        return -1;
+    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
+    Py_INCREF(op->defaults_tuple);
+    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
+    Py_INCREF(op->defaults_kwdict);
+    #else
+    op->defaults_tuple = PySequence_ITEM(res, 0);
+    if (unlikely(!op->defaults_tuple)) result = -1;
+    else {
+        op->defaults_kwdict = PySequence_ITEM(res, 1);
+        if (unlikely(!op->defaults_kwdict)) result = -1;
+    }
+    #endif
+    Py_DECREF(res);
+    return result;
+}
+static int
+__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) {
+    PyObject* tmp;
+    if (!value) {
+        value = Py_None;
+    } else if (value != Py_None && !PyTuple_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "__defaults__ must be set to a tuple object");
+        return -1;
+    }
+    Py_INCREF(value);
+    tmp = op->defaults_tuple;
+    op->defaults_tuple = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) {
+    PyObject* result = op->defaults_tuple;
+    if (unlikely(!result)) {
+        if (op->defaults_getter) {
+            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
+            result = op->defaults_tuple;
+        } else {
+            result = Py_None;
+        }
+    }
+    Py_INCREF(result);
+    return result;
+}
+static int
+__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) {
+    PyObject* tmp;
+    if (!value) {
+        value = Py_None;
+    } else if (value != Py_None && !PyDict_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "__kwdefaults__ must be set to a dict object");
+        return -1;
+    }
+    Py_INCREF(value);
+    tmp = op->defaults_kwdict;
+    op->defaults_kwdict = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) {
+    PyObject* result = op->defaults_kwdict;
+    if (unlikely(!result)) {
+        if (op->defaults_getter) {
+            if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
+            result = op->defaults_kwdict;
+        } else {
+            result = Py_None;
+        }
+    }
+    Py_INCREF(result);
+    return result;
+}
+static int
+__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) {
+    PyObject* tmp;
+    if (!value || value == Py_None) {
+        value = NULL;
+    } else if (!PyDict_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "__annotations__ must be set to a dict object");
+        return -1;
+    }
+    Py_XINCREF(value);
+    tmp = op->func_annotations;
+    op->func_annotations = value;
+    Py_XDECREF(tmp);
+    return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) {
+    PyObject* result = op->func_annotations;
+    if (unlikely(!result)) {
+        result = PyDict_New();
+        if (unlikely(!result)) return NULL;
+        op->func_annotations = result;
+    }
+    Py_INCREF(result);
+    return result;
+}
+static PyGetSetDef __pyx_CyFunction_getsets[] = {
+    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
+    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
+    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
+    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
+    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
+    {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0},
+    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
+    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
+    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
+    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
+    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
+    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
+    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
+    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
+    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
+    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
+    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
+    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
+    {0, 0, 0, 0, 0}
+};
+static PyMemberDef __pyx_CyFunction_members[] = {
+    {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0},
+    {0, 0, 0,  0, 0}
+};
+static PyObject *
+__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args)
+{
+#if PY_MAJOR_VERSION >= 3
+    return PyUnicode_FromString(m->func.m_ml->ml_name);
+#else
+    return PyString_FromString(m->func.m_ml->ml_name);
+#endif
+}
+static PyMethodDef __pyx_CyFunction_methods[] = {
+    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
+    {0, 0, 0, 0}
+};
+#if PY_VERSION_HEX < 0x030500A0
+#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
+#else
+#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist)
+#endif
+static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname,
+                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
+    __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type);
+    if (op == NULL)
+        return NULL;
+    op->flags = flags;
+    __Pyx_CyFunction_weakreflist(op) = NULL;
+    op->func.m_ml = ml;
+    op->func.m_self = (PyObject *) op;
+    Py_XINCREF(closure);
+    op->func_closure = closure;
+    Py_XINCREF(module);
+    op->func.m_module = module;
+    op->func_dict = NULL;
+    op->func_name = NULL;
+    Py_INCREF(qualname);
+    op->func_qualname = qualname;
+    op->func_doc = NULL;
+    op->func_classobj = NULL;
+    op->func_globals = globals;
+    Py_INCREF(op->func_globals);
+    Py_XINCREF(code);
+    op->func_code = code;
+    op->defaults_pyobjects = 0;
+    op->defaults = NULL;
+    op->defaults_tuple = NULL;
+    op->defaults_kwdict = NULL;
+    op->defaults_getter = NULL;
+    op->func_annotations = NULL;
+    PyObject_GC_Track(op);
+    return (PyObject *) op;
+}
+static int
+__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
+{
+    Py_CLEAR(m->func_closure);
+    Py_CLEAR(m->func.m_module);
+    Py_CLEAR(m->func_dict);
+    Py_CLEAR(m->func_name);
+    Py_CLEAR(m->func_qualname);
+    Py_CLEAR(m->func_doc);
+    Py_CLEAR(m->func_globals);
+    Py_CLEAR(m->func_code);
+    Py_CLEAR(m->func_classobj);
+    Py_CLEAR(m->defaults_tuple);
+    Py_CLEAR(m->defaults_kwdict);
+    Py_CLEAR(m->func_annotations);
+    if (m->defaults) {
+        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
+        int i;
+        for (i = 0; i < m->defaults_pyobjects; i++)
+            Py_XDECREF(pydefaults[i]);
+        PyObject_Free(m->defaults);
+        m->defaults = NULL;
+    }
+    return 0;
+}
+static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
+{
+    PyObject_GC_UnTrack(m);
+    if (__Pyx_CyFunction_weakreflist(m) != NULL)
+        PyObject_ClearWeakRefs((PyObject *) m);
+    __Pyx_CyFunction_clear(m);
+    PyObject_GC_Del(m);
+}
+static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
+{
+    Py_VISIT(m->func_closure);
+    Py_VISIT(m->func.m_module);
+    Py_VISIT(m->func_dict);
+    Py_VISIT(m->func_name);
+    Py_VISIT(m->func_qualname);
+    Py_VISIT(m->func_doc);
+    Py_VISIT(m->func_globals);
+    Py_VISIT(m->func_code);
+    Py_VISIT(m->func_classobj);
+    Py_VISIT(m->defaults_tuple);
+    Py_VISIT(m->defaults_kwdict);
+    if (m->defaults) {
+        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
+        int i;
+        for (i = 0; i < m->defaults_pyobjects; i++)
+            Py_VISIT(pydefaults[i]);
+    }
+    return 0;
+}
+static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type)
+{
+    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+    if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) {
+        Py_INCREF(func);
+        return func;
+    }
+    if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) {
+        if (type == NULL)
+            type = (PyObject *)(Py_TYPE(obj));
+        return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type)));
+    }
+    if (obj == Py_None)
+        obj = NULL;
+    return __Pyx_PyMethod_New(func, obj, type);
+}
+static PyObject*
+__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
+{
+#if PY_MAJOR_VERSION >= 3
+    return PyUnicode_FromFormat("<cyfunction %U at %p>",
+                                op->func_qualname, (void *)op);
+#else
+    return PyString_FromFormat("<cyfunction %s at %p>",
+                               PyString_AsString(op->func_qualname), (void *)op);
+#endif
+}
+static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
+    PyCFunctionObject* f = (PyCFunctionObject*)func;
+    PyCFunction meth = f->m_ml->ml_meth;
+    Py_ssize_t size;
+    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
+    case METH_VARARGS:
+        if (likely(kw == NULL || PyDict_Size(kw) == 0))
+            return (*meth)(self, arg);
+        break;
+    case METH_VARARGS | METH_KEYWORDS:
+        return (*(PyCFunctionWithKeywords)meth)(self, arg, kw);
+    case METH_NOARGS:
+        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+            size = PyTuple_GET_SIZE(arg);
+            if (likely(size == 0))
+                return (*meth)(self, NULL);
+            PyErr_Format(PyExc_TypeError,
+                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                f->m_ml->ml_name, size);
+            return NULL;
+        }
+        break;
+    case METH_O:
+        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+            size = PyTuple_GET_SIZE(arg);
+            if (likely(size == 1)) {
+                PyObject *result, *arg0 = PySequence_ITEM(arg, 0);
+                if (unlikely(!arg0)) return NULL;
+                result = (*meth)(self, arg0);
+                Py_DECREF(arg0);
+                return result;
+            }
+            PyErr_Format(PyExc_TypeError,
+                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                f->m_ml->ml_name, size);
+            return NULL;
+        }
+        break;
+    default:
+        PyErr_SetString(PyExc_SystemError, "Bad call flags in "
+                        "__Pyx_CyFunction_Call. METH_OLDARGS is no "
+                        "longer supported!");
+        return NULL;
+    }
+    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
+                 f->m_ml->ml_name);
+    return NULL;
+}
+static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
+}
+static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
+    PyObject *result;
+    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
+    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
+        Py_ssize_t argc;
+        PyObject *new_args;
+        PyObject *self;
+        argc = PyTuple_GET_SIZE(args);
+        new_args = PyTuple_GetSlice(args, 1, argc);
+        if (unlikely(!new_args))
+            return NULL;
+        self = PyTuple_GetItem(args, 0);
+        if (unlikely(!self)) {
+            Py_DECREF(new_args);
+            return NULL;
+        }
+        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
+        Py_DECREF(new_args);
+    } else {
+        result = __Pyx_CyFunction_Call(func, args, kw);
+    }
+    return result;
+}
+static PyTypeObject __pyx_CyFunctionType_type = {
+    PyVarObject_HEAD_INIT(0, 0)
+    "cython_function_or_method",
+    sizeof(__pyx_CyFunctionObject),
+    0,
+    (destructor) __Pyx_CyFunction_dealloc,
+    0,
+    0,
+    0,
+#if PY_MAJOR_VERSION < 3
+    0,
+#else
+    0,
+#endif
+    (reprfunc) __Pyx_CyFunction_repr,
+    0,
+    0,
+    0,
+    0,
+    __Pyx_CyFunction_CallAsMethod,
+    0,
+    0,
+    0,
+    0,
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
+    0,
+    (traverseproc) __Pyx_CyFunction_traverse,
+    (inquiry) __Pyx_CyFunction_clear,
+    0,
+#if PY_VERSION_HEX < 0x030500A0
+    offsetof(__pyx_CyFunctionObject, func_weakreflist),
+#else
+    offsetof(PyCFunctionObject, m_weakreflist),
+#endif
+    0,
+    0,
+    __pyx_CyFunction_methods,
+    __pyx_CyFunction_members,
+    __pyx_CyFunction_getsets,
+    0,
+    0,
+    __Pyx_CyFunction_descr_get,
+    0,
+    offsetof(__pyx_CyFunctionObject, func_dict),
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+    0,
+#if PY_VERSION_HEX >= 0x030400a1
+    0,
+#endif
+};
+static int __pyx_CyFunction_init(void) {
+    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
+    if (__pyx_CyFunctionType == NULL) {
+        return -1;
+    }
+    return 0;
+}
+static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
+    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+    m->defaults = PyObject_Malloc(size);
+    if (!m->defaults)
+        return PyErr_NoMemory();
+    memset(m->defaults, 0, size);
+    m->defaults_pyobjects = pyobjects;
+    return m->defaults;
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
+    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+    m->defaults_tuple = tuple;
+    Py_INCREF(tuple);
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
+    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+    m->defaults_kwdict = dict;
+    Py_INCREF(dict);
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
+    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+    m->func_annotations = dict;
+    Py_INCREF(dict);
+}
+
+/* BufferFallbackError */
+            static void __Pyx_RaiseBufferFallbackError(void) {
+  PyErr_SetString(PyExc_ValueError,
+     "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!");
+}
+
+/* None */
+            static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
+    Py_ssize_t q = a / b;
+    Py_ssize_t r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+/* BufferIndexError */
+            static void __Pyx_RaiseBufferIndexError(int axis) {
+  PyErr_Format(PyExc_IndexError,
+     "Out of bounds on buffer access (axis %d)", axis);
+}
+
+/* RaiseTooManyValuesToUnpack */
+            static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+    PyErr_Format(PyExc_ValueError,
+                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
+}
+
+/* RaiseNeedMoreValuesToUnpack */
+            static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+    PyErr_Format(PyExc_ValueError,
+                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+                 index, (index == 1) ? "" : "s");
+}
+
+/* RaiseNoneIterError */
+            static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
+    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+}
+
+/* SaveResetException */
+            #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+}
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+#endif
+
+/* PyErrExceptionMatches */
+            #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
+    PyObject *exc_type = tstate->curexc_type;
+    if (exc_type == err) return 1;
+    if (unlikely(!exc_type)) return 0;
+    return PyErr_GivenExceptionMatches(exc_type, err);
+}
+#endif
+
+/* GetException */
+            #if CYTHON_FAST_THREAD_STATE
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+#endif
+    PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    local_type = tstate->curexc_type;
+    local_value = tstate->curexc_value;
+    local_tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_FAST_THREAD_STATE
+    if (unlikely(tstate->curexc_type))
+#else
+    if (unlikely(PyErr_Occurred()))
+#endif
+        goto bad;
+    #if PY_MAJOR_VERSION >= 3
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
+    }
+    #endif
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
+    *type = local_type;
+    *value = local_value;
+    *tb = local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = local_type;
+    tstate->exc_value = local_value;
+    tstate->exc_traceback = local_tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+    return 0;
+bad:
+    *type = 0;
+    *value = 0;
+    *tb = 0;
+    Py_XDECREF(local_type);
+    Py_XDECREF(local_value);
+    Py_XDECREF(local_tb);
+    return -1;
+}
+
+/* Import */
+              static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
+    PyObject *empty_list = 0;
+    PyObject *module = 0;
+    PyObject *global_dict = 0;
+    PyObject *empty_dict = 0;
+    PyObject *list;
+    #if PY_VERSION_HEX < 0x03030000
+    PyObject *py_import;
+    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
+    if (!py_import)
+        goto bad;
+    #endif
+    if (from_list)
+        list = from_list;
+    else {
+        empty_list = PyList_New(0);
+        if (!empty_list)
+            goto bad;
+        list = empty_list;
+    }
+    global_dict = PyModule_GetDict(__pyx_m);
+    if (!global_dict)
+        goto bad;
+    empty_dict = PyDict_New();
+    if (!empty_dict)
+        goto bad;
+    {
+        #if PY_MAJOR_VERSION >= 3
+        if (level == -1) {
+            if (strchr(__Pyx_MODULE_NAME, '.')) {
+                #if PY_VERSION_HEX < 0x03030000
+                PyObject *py_level = PyInt_FromLong(1);
+                if (!py_level)
+                    goto bad;
+                module = PyObject_CallFunctionObjArgs(py_import,
+                    name, global_dict, empty_dict, list, py_level, NULL);
+                Py_DECREF(py_level);
+                #else
+                module = PyImport_ImportModuleLevelObject(
+                    name, global_dict, empty_dict, list, 1);
+                #endif
+                if (!module) {
+                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
+                        goto bad;
+                    PyErr_Clear();
+                }
+            }
+            level = 0;
+        }
+        #endif
+        if (!module) {
+            #if PY_VERSION_HEX < 0x03030000
+            PyObject *py_level = PyInt_FromLong(level);
+            if (!py_level)
+                goto bad;
+            module = PyObject_CallFunctionObjArgs(py_import,
+                name, global_dict, empty_dict, list, py_level, NULL);
+            Py_DECREF(py_level);
+            #else
+            module = PyImport_ImportModuleLevelObject(
+                name, global_dict, empty_dict, list, level);
+            #endif
+        }
+    }
+bad:
+    #if PY_VERSION_HEX < 0x03030000
+    Py_XDECREF(py_import);
+    #endif
+    Py_XDECREF(empty_list);
+    Py_XDECREF(empty_dict);
+    return module;
+}
+
+/* CodeObjectCache */
+              static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = start + (end - start) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
+/* AddTraceback */
+              #include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    #if PY_MAJOR_VERSION < 3
+    py_srcfile = PyString_FromString(filename);
+    #else
+    py_srcfile = PyUnicode_FromString(filename);
+    #endif
+    if (!py_srcfile) goto bad;
+    if (c_line) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
+    if (!py_funcname) goto bad;
+    py_code = __Pyx_PyCode_New(
+        0,
+        0,
+        0,
+        0,
+        0,
+        __pyx_empty_bytes, /*PyObject *code,*/
+        __pyx_empty_tuple, /*PyObject *consts,*/
+        __pyx_empty_tuple, /*PyObject *names,*/
+        __pyx_empty_tuple, /*PyObject *varnames,*/
+        __pyx_empty_tuple, /*PyObject *freevars,*/
+        __pyx_empty_tuple, /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        py_line,
+        __pyx_empty_bytes  /*PyObject *lnotab*/
+    );
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyFrameObject *py_frame = 0;
+    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
+    }
+    py_frame = PyFrame_New(
+        PyThreadState_GET(), /*PyThreadState *tstate,*/
+        py_code,             /*PyCodeObject *code,*/
+        __pyx_d,      /*PyObject *globals,*/
+        0                    /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
+
+#if PY_MAJOR_VERSION < 3
+static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
+    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
+        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
+    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
+    return -1;
+}
+static void __Pyx_ReleaseBuffer(Py_buffer *view) {
+    PyObject *obj = view->obj;
+    if (!obj) return;
+    if (PyObject_CheckBuffer(obj)) {
+        PyBuffer_Release(view);
+        return;
+    }
+        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
+    Py_DECREF(obj);
+    view->obj = NULL;
+}
+#endif
+
+
+              /* CIntFromPyVerify */
+              #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
+#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
+#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
+    {\
+        func_type value = func_value;\
+        if (sizeof(target_type) < sizeof(func_type)) {\
+            if (unlikely(value != (func_type) (target_type) value)) {\
+                func_type zero = 0;\
+                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
+                    return (target_type) -1;\
+                if (is_unsigned && unlikely(value < zero))\
+                    goto raise_neg_overflow;\
+                else\
+                    goto raise_overflow;\
+            }\
+        }\
+        return (target_type) value;\
+    }
+
+/* CIntToPy */
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value) {
+    const siz neg_one = (siz) -1, const_zero = (siz) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(siz) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(siz) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(siz) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(siz),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntToPy */
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) -1, const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntToPy */
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) {
+    const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(Py_intptr_t) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(Py_intptr_t) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t),
+                                     little, !is_unsigned);
+    }
+}
+
+/* Declarations */
+              #if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return ::std::complex< float >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return x + y*(__pyx_t_float_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      __pyx_t_float_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+/* Arithmetic */
+              #if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    #if 1
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else if (fabsf(b.real) >= fabsf(b.imag)) {
+            if (b.real == 0 && b.imag == 0) {
+                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
+            } else {
+                float r = b.imag / b.real;
+                float s = 1.0 / (b.real + b.imag * r);
+                return __pyx_t_float_complex_from_parts(
+                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
+            }
+        } else {
+            float r = b.real / b.imag;
+            float s = 1.0 / (b.imag + b.real * r);
+            return __pyx_t_float_complex_from_parts(
+                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
+        }
+    }
+    #else
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else {
+            float denom = b.real * b.real + b.imag * b.imag;
+            return __pyx_t_float_complex_from_parts(
+                (a.real * b.real + a.imag * b.imag) / denom,
+                (a.imag * b.real - a.real * b.imag) / denom);
+        }
+    }
+    #endif
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrtf(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypotf(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+            __pyx_t_float_complex z;
+            float r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    float denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        z = __Pyx_c_prod_float(a, a);
+                        return __Pyx_c_prod_float(a, a);
+                    case 3:
+                        z = __Pyx_c_prod_float(a, a);
+                        return __Pyx_c_prod_float(z, a);
+                    case 4:
+                        z = __Pyx_c_prod_float(a, a);
+                        return __Pyx_c_prod_float(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                } else if (b.imag == 0) {
+                    z.real = powf(a.real, b.real);
+                    z.imag = 0;
+                    return z;
+                } else if (a.real > 0) {
+                    r = a.real;
+                    theta = 0;
+                } else {
+                    r = -a.real;
+                    theta = atan2f(0, -1);
+                }
+            } else {
+                r = __Pyx_c_abs_float(a);
+                theta = atan2f(a.imag, a.real);
+            }
+            lnr = logf(r);
+            z_r = expf(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cosf(z_theta);
+            z.imag = z_r * sinf(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+/* Declarations */
+              #if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return ::std::complex< double >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return x + y*(__pyx_t_double_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      __pyx_t_double_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+/* Arithmetic */
+              #if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    #if 1
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else if (fabs(b.real) >= fabs(b.imag)) {
+            if (b.real == 0 && b.imag == 0) {
+                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
+            } else {
+                double r = b.imag / b.real;
+                double s = 1.0 / (b.real + b.imag * r);
+                return __pyx_t_double_complex_from_parts(
+                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
+            }
+        } else {
+            double r = b.real / b.imag;
+            double s = 1.0 / (b.imag + b.real * r);
+            return __pyx_t_double_complex_from_parts(
+                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
+        }
+    }
+    #else
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else {
+            double denom = b.real * b.real + b.imag * b.imag;
+            return __pyx_t_double_complex_from_parts(
+                (a.real * b.real + a.imag * b.imag) / denom,
+                (a.imag * b.real - a.real * b.imag) / denom);
+        }
+    }
+    #endif
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrt(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypot(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+            __pyx_t_double_complex z;
+            double r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    double denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        z = __Pyx_c_prod_double(a, a);
+                        return __Pyx_c_prod_double(a, a);
+                    case 3:
+                        z = __Pyx_c_prod_double(a, a);
+                        return __Pyx_c_prod_double(z, a);
+                    case 4:
+                        z = __Pyx_c_prod_double(a, a);
+                        return __Pyx_c_prod_double(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                } else if (b.imag == 0) {
+                    z.real = pow(a.real, b.real);
+                    z.imag = 0;
+                    return z;
+                } else if (a.real > 0) {
+                    r = a.real;
+                    theta = 0;
+                } else {
+                    r = -a.real;
+                    theta = atan2(0, -1);
+                }
+            } else {
+                r = __Pyx_c_abs_double(a);
+                theta = atan2(a.imag, a.real);
+            }
+            lnr = log(r);
+            z_r = exp(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cos(z_theta);
+            z.imag = z_r * sin(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+/* CIntToPy */
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
+    const int neg_one = (int) -1, const_zero = (int) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(int) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(int) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(int),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntToPy */
+              static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {
+    const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(enum NPY_TYPES) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(enum NPY_TYPES) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntFromPy */
+              static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *x) {
+    const siz neg_one = (siz) -1, const_zero = (siz) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(siz) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(siz, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (siz) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (siz) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(siz, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(siz) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) >= 2 * PyLong_SHIFT) {
+                            return (siz) (((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(siz) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) >= 3 * PyLong_SHIFT) {
+                            return (siz) (((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(siz) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) >= 4 * PyLong_SHIFT) {
+                            return (siz) (((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (siz) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(siz) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (siz) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(siz, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(siz,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(siz) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
+                            return (siz) (((siz)-1)*(((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(siz) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
+                            return (siz) ((((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
+                            return (siz) (((siz)-1)*(((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(siz) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
+                            return (siz) ((((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) {
+                            return (siz) (((siz)-1)*(((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(siz) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) {
+                            return (siz) ((((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(siz) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(siz, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(siz, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            siz val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (siz) -1;
+        }
+    } else {
+        siz val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (siz) -1;
+        val = __Pyx_PyInt_As_siz(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to siz");
+    return (siz) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to siz");
+    return (siz) -1;
+}
+
+/* CIntFromPy */
+              static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) {
+    const size_t neg_one = (size_t) -1, const_zero = (size_t) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(size_t) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (size_t) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (size_t) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) {
+                            return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) {
+                            return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) {
+                            return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (size_t) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(size_t) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (size_t) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(size_t,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
+                            return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
+                            return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
+                            return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
+                            return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
+                            return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
+                            return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(size_t) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            size_t val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (size_t) -1;
+        }
+    } else {
+        size_t val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (size_t) -1;
+        val = __Pyx_PyInt_As_size_t(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to size_t");
+    return (size_t) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to size_t");
+    return (size_t) -1;
+}
+
+/* CIntFromPy */
+              static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) -1, const_zero = (int) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (int) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
+                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
+                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
+                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (int) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (int) -1;
+        }
+    } else {
+        int val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to int");
+    return (int) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to int");
+    return (int) -1;
+}
+
+/* CIntFromPy */
+              static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) -1, const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
+                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
+                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
+                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (long) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (long) -1;
+        }
+    } else {
+        long val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to long");
+    return (long) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to long");
+    return (long) -1;
+}
+
+/* CheckBinaryVersion */
+              static int __Pyx_check_binary_version(void) {
+    char ctversion[4], rtversion[4];
+    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+        char message[200];
+        PyOS_snprintf(message, sizeof(message),
+                      "compiletime version %s of module '%.100s' "
+                      "does not match runtime version %s",
+                      ctversion, __Pyx_MODULE_NAME, rtversion);
+        return PyErr_WarnEx(NULL, message, 1);
+    }
+    return 0;
+}
+
+/* ModuleImport */
+              #ifndef __PYX_HAVE_RT_ImportModule
+#define __PYX_HAVE_RT_ImportModule
+static PyObject *__Pyx_ImportModule(const char *name) {
+    PyObject *py_name = 0;
+    PyObject *py_module = 0;
+    py_name = __Pyx_PyIdentifier_FromString(name);
+    if (!py_name)
+        goto bad;
+    py_module = PyImport_Import(py_name);
+    Py_DECREF(py_name);
+    return py_module;
+bad:
+    Py_XDECREF(py_name);
+    return 0;
+}
+#endif
+
+/* TypeImport */
+              #ifndef __PYX_HAVE_RT_ImportType
+#define __PYX_HAVE_RT_ImportType
+static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
+    size_t size, int strict)
+{
+    PyObject *py_module = 0;
+    PyObject *result = 0;
+    PyObject *py_name = 0;
+    char warning[200];
+    Py_ssize_t basicsize;
+#ifdef Py_LIMITED_API
+    PyObject *py_basicsize;
+#endif
+    py_module = __Pyx_ImportModule(module_name);
+    if (!py_module)
+        goto bad;
+    py_name = __Pyx_PyIdentifier_FromString(class_name);
+    if (!py_name)
+        goto bad;
+    result = PyObject_GetAttr(py_module, py_name);
+    Py_DECREF(py_name);
+    py_name = 0;
+    Py_DECREF(py_module);
+    py_module = 0;
+    if (!result)
+        goto bad;
+    if (!PyType_Check(result)) {
+        PyErr_Format(PyExc_TypeError,
+            "%.200s.%.200s is not a type object",
+            module_name, class_name);
+        goto bad;
+    }
+#ifndef Py_LIMITED_API
+    basicsize = ((PyTypeObject *)result)->tp_basicsize;
+#else
+    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
+    if (!py_basicsize)
+        goto bad;
+    basicsize = PyLong_AsSsize_t(py_basicsize);
+    Py_DECREF(py_basicsize);
+    py_basicsize = 0;
+    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
+        goto bad;
+#endif
+    if (!strict && (size_t)basicsize > size) {
+        PyOS_snprintf(warning, sizeof(warning),
+            "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd",
+            module_name, class_name, basicsize, size);
+        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
+    }
+    else if ((size_t)basicsize != size) {
+        PyErr_Format(PyExc_ValueError,
+            "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd",
+            module_name, class_name, basicsize, size);
+        goto bad;
+    }
+    return (PyTypeObject *)result;
+bad:
+    Py_XDECREF(py_module);
+    Py_XDECREF(result);
+    return NULL;
+}
+#endif
+
+/* InitStrings */
+              static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else
+        if (t->is_unicode | t->is_str) {
+            if (t->intern) {
+                *t->p = PyUnicode_InternFromString(t->s);
+            } else if (t->encoding) {
+                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+            } else {
+                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+            }
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+    Py_ssize_t ignore;
+    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+            PyUnicode_Check(o)) {
+#if PY_VERSION_HEX < 0x03030000
+        char* defenc_c;
+        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+        if (!defenc) return NULL;
+        defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        {
+            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+            char* c;
+            for (c = defenc_c; c < end; c++) {
+                if ((unsigned char) (*c) >= 128) {
+                    PyUnicode_AsASCIIString(o);
+                    return NULL;
+                }
+            }
+        }
+#endif
+        *length = PyBytes_GET_SIZE(defenc);
+        return defenc_c;
+#else
+        if (__Pyx_PyUnicode_READY(o) == -1) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        if (PyUnicode_IS_ASCII(o)) {
+            *length = PyUnicode_GET_LENGTH(o);
+            return PyUnicode_AsUTF8(o);
+        } else {
+            PyUnicode_AsASCIIString(o);
+            return NULL;
+        }
+#else
+        return PyUnicode_AsUTF8AndSize(o, length);
+#endif
+#endif
+    } else
+#endif
+#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
+    {
+        char* result;
+        int r = PyBytes_AsStringAndSize(o, &result, length);
+        if (unlikely(r < 0)) {
+            return NULL;
+        } else {
+            return result;
+        }
+    }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+   else return PyObject_IsTrue(x);
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
+#if CYTHON_USE_TYPE_SLOTS
+  PyNumberMethods *m;
+#endif
+  const char *name = NULL;
+  PyObject *res = NULL;
+#if PY_MAJOR_VERSION < 3
+  if (PyInt_Check(x) || PyLong_Check(x))
+#else
+  if (PyLong_Check(x))
+#endif
+    return __Pyx_NewRef(x);
+#if CYTHON_USE_TYPE_SLOTS
+  m = Py_TYPE(x)->tp_as_number;
+  #if PY_MAJOR_VERSION < 3
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Int(x);
+  }
+  else if (m && m->nb_long) {
+    name = "long";
+    res = PyNumber_Long(x);
+  }
+  #else
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Long(x);
+  }
+  #endif
+#else
+  res = PyNumber_Int(x);
+#endif
+  if (res) {
+#if PY_MAJOR_VERSION < 3
+    if (!PyInt_Check(res) && !PyLong_Check(res)) {
+#else
+    if (!PyLong_Check(res)) {
+#endif
+      PyErr_Format(PyExc_TypeError,
+                   "__%.4s__ returned non-%.4s (type %.200s)",
+                   name, name, Py_TYPE(res)->tp_name);
+      Py_DECREF(res);
+      return NULL;
+    }
+  }
+  else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_TypeError,
+                    "an integer is required");
+  }
+  return res;
+}
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+  Py_ssize_t ival;
+  PyObject *x;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_CheckExact(b))) {
+    if (sizeof(Py_ssize_t) >= sizeof(long))
+        return PyInt_AS_LONG(b);
+    else
+        return PyInt_AsSsize_t(x);
+  }
+#endif
+  if (likely(PyLong_CheckExact(b))) {
+    #if CYTHON_USE_PYLONG_INTERNALS
+    const digit* digits = ((PyLongObject*)b)->ob_digit;
+    const Py_ssize_t size = Py_SIZE(b);
+    if (likely(__Pyx_sst_abs(size) <= 1)) {
+        ival = likely(size) ? digits[0] : 0;
+        if (size == -1) ival = -ival;
+        return ival;
+    } else {
+      switch (size) {
+         case 2:
+           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -2:
+           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case 3:
+           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -3:
+           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case 4:
+           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -4:
+           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+      }
+    }
+    #endif
+    return PyLong_AsSsize_t(b);
+  }
+  x = PyNumber_Index(b);
+  if (!x) return -1;
+  ival = PyInt_AsSsize_t(x);
+  Py_DECREF(x);
+  return ival;
+}
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+    return PyInt_FromSize_t(ival);
+}
+
+
+#endif /* Py_PYTHON_H */
diff --git a/examples/rfcn/lib/pycocotools/_mask.pyx b/examples/rfcn/lib/pycocotools/_mask.pyx
new file mode 100644
index 000000000..e08f1f480
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/_mask.pyx
@@ -0,0 +1,291 @@
+# distutils: language = c
+# distutils: sources = ../MatlabAPI/private/maskApi.c
+
+#**************************************************************************
+# Microsoft COCO Toolbox.      version 2.0
+# Data, paper, and tutorials available at:  http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+# Licensed under the Simplified BSD License [see coco/license.txt]
+#**************************************************************************
+
+__author__ = 'tsungyi'
+
+# import both Python-level and C-level symbols of Numpy
+# the API uses Numpy to interface C and Python
+import numpy as np
+cimport numpy as np
+from libc.stdlib cimport malloc, free
+
+# intialized Numpy. must do.
+np.import_array()
+
+# import numpy C function
+# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management
+cdef extern from "numpy/arrayobject.h":
+    void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)
+
+# Declare the prototype of the C functions in MaskApi.h
+cdef extern from "maskApi.h":
+    ctypedef unsigned int uint
+    ctypedef unsigned long siz
+    ctypedef unsigned char byte
+    ctypedef double* BB
+    ctypedef struct RLE:
+        siz h,
+        siz w,
+        siz m,
+        uint* cnts,
+    void rlesInit( RLE **R, siz n )
+    void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n )
+    void rleDecode( const RLE *R, byte *mask, siz n )
+    void rleMerge( const RLE *R, RLE *M, siz n, bint intersect )
+    void rleArea( const RLE *R, siz n, uint *a )
+    void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o )
+    void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o )
+    void rleToBbox( const RLE *R, BB bb, siz n )
+    void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n )
+    void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w )
+    char* rleToString( const RLE *R )
+    void rleFrString( RLE *R, char *s, siz h, siz w )
+
+# python class to wrap RLE array in C
+# the class handles the memory allocation and deallocation
+cdef class RLEs:
+    cdef RLE *_R
+    cdef siz _n
+
+    def __cinit__(self, siz n =0):
+        rlesInit(&self._R, n)
+        self._n = n
+
+    # free the RLE array here
+    def __dealloc__(self):
+        if self._R is not NULL:
+            for i in range(self._n):
+                free(self._R[i].cnts)
+            free(self._R)
+    def __getattr__(self, key):
+        if key == 'n':
+            return self._n
+        raise AttributeError(key)
+
+# python class to wrap Mask array in C
+# the class handles the memory allocation and deallocation
+cdef class Masks:
+    cdef byte *_mask
+    cdef siz _h
+    cdef siz _w
+    cdef siz _n
+
+    def __cinit__(self, h, w, n):
+        self._mask = <byte*> malloc(h*w*n* sizeof(byte))
+        self._h = h
+        self._w = w
+        self._n = n
+    # def __dealloc__(self):
+        # the memory management of _mask has been passed to np.ndarray
+        # it doesn't need to be freed here
+
+    # called when passing into np.array() and return an np.ndarray in column-major order
+    def __array__(self):
+        cdef np.npy_intp shape[1]
+        shape[0] = <np.npy_intp> self._h*self._w*self._n
+        # Create a 1D array, and reshape it to fortran/Matlab column-major array
+        ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F')
+        # The _mask allocated by Masks is now handled by ndarray
+        PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA)
+        return ndarray
+
+# internal conversion from Python RLEs object to compressed RLE format
+def _toString(RLEs Rs):
+    cdef siz n = Rs.n
+    cdef bytes py_string
+    cdef char* c_string
+    objs = []
+    for i in range(n):
+        c_string = rleToString( <RLE*> &Rs._R[i] )
+        py_string = c_string
+        objs.append({
+            'size': [Rs._R[i].h, Rs._R[i].w],
+            'counts': py_string
+        })
+        free(c_string)
+    return objs
+
+# internal conversion from compressed RLE format to Python RLEs object
+def _frString(rleObjs):
+    cdef siz n = len(rleObjs)
+    Rs = RLEs(n)
+    cdef bytes py_string
+    cdef char* c_string
+    for i, obj in enumerate(rleObjs):
+        py_string = str(obj['counts'])
+        c_string = py_string
+        rleFrString( <RLE*> &Rs._R[i], <char*> c_string, obj['size'][0], obj['size'][1] )
+    return Rs
+
+# encode mask to RLEs objects
+# list of RLE string can be generated by RLEs member function
+def encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask):
+    h, w, n = mask.shape[0], mask.shape[1], mask.shape[2]
+    cdef RLEs Rs = RLEs(n)
+    rleEncode(Rs._R,<byte*>mask.data,h,w,n)
+    objs = _toString(Rs)
+    return objs
+
+# decode mask from compressed list of RLE string or RLEs object
+def decode(rleObjs):
+    cdef RLEs Rs = _frString(rleObjs)
+    h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n
+    masks = Masks(h, w, n)
+    rleDecode( <RLE*>Rs._R, masks._mask, n );
+    return np.array(masks)
+
+def merge(rleObjs, bint intersect=0):
+    cdef RLEs Rs = _frString(rleObjs)
+    cdef RLEs R = RLEs(1)
+    rleMerge(<RLE*>Rs._R, <RLE*> R._R, <siz> Rs._n, intersect)
+    obj = _toString(R)[0]
+    return obj
+
+def area(rleObjs):
+    cdef RLEs Rs = _frString(rleObjs)
+    cdef uint* _a = <uint*> malloc(Rs._n* sizeof(uint))
+    rleArea(Rs._R, Rs._n, _a)
+    cdef np.npy_intp shape[1]
+    shape[0] = <np.npy_intp> Rs._n
+    a = np.array((Rs._n, ), dtype=np.uint8)
+    a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a)
+    PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA)
+    return a
+
+# iou computation. support function overload (RLEs-RLEs and bbox-bbox).
+def iou( dt, gt, pyiscrowd ):
+    def _preproc(objs):
+        if len(objs) == 0:
+            return objs
+        if type(objs) == np.ndarray:
+            if len(objs.shape) == 1:
+                objs = objs.reshape((objs[0], 1))
+            # check if it's Nx4 bbox
+            if not len(objs.shape) == 2 or not objs.shape[1] == 4:
+                raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension')
+            objs = objs.astype(np.double)
+        elif type(objs) == list:
+            # check if list is in box format and convert it to np.ndarray
+            isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs]))
+            isrle = np.all(np.array([type(obj) == dict for obj in objs]))
+            if isbox:
+                objs = np.array(objs, dtype=np.double)
+                if len(objs.shape) == 1:
+                    objs = objs.reshape((1,objs.shape[0]))
+            elif isrle:
+                objs = _frString(objs)
+            else:
+                raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])')
+        else:
+            raise Exception('unrecognized type.  The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.')
+        return objs
+    def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t,  ndim=1] _iou):
+        rleIou( <RLE*> dt._R, <RLE*> gt._R, m, n, <byte*> iscrowd.data, <double*> _iou.data )
+    def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou):
+        bbIou( <BB> dt.data, <BB> gt.data, m, n, <byte*> iscrowd.data, <double*>_iou.data )
+    def _len(obj):
+        cdef siz N = 0
+        if type(obj) == RLEs:
+            N = obj.n
+        elif len(obj)==0:
+            pass
+        elif type(obj) == np.ndarray:
+            N = obj.shape[0]
+        return N
+    # convert iscrowd to numpy array
+    cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8)
+    # simple type checking
+    cdef siz m, n
+    dt = _preproc(dt)
+    gt = _preproc(gt)
+    m = _len(dt)
+    n = _len(gt)
+    if m == 0 or n == 0:
+        return []
+    if not type(dt) == type(gt):
+        raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray')
+
+    # define local variables
+    cdef double* _iou = <double*> 0
+    cdef np.npy_intp shape[1]
+    # check type and assign iou function
+    if type(dt) == RLEs:
+        _iouFun = _rleIou
+    elif type(dt) == np.ndarray:
+        _iouFun = _bbIou
+    else:
+        raise Exception('input data type not allowed.')
+    _iou = <double*> malloc(m*n* sizeof(double))
+    iou = np.zeros((m*n, ), dtype=np.double)
+    shape[0] = <np.npy_intp> m*n
+    iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou)
+    PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA)
+    _iouFun(dt, gt, iscrowd, m, n, iou)
+    return iou.reshape((m,n), order='F')
+
+def toBbox( rleObjs ):
+    cdef RLEs Rs = _frString(rleObjs)
+    cdef siz n = Rs.n
+    cdef BB _bb = <BB> malloc(4*n* sizeof(double))
+    rleToBbox( <const RLE*> Rs._R, _bb, n )
+    cdef np.npy_intp shape[1]
+    shape[0] = <np.npy_intp> 4*n
+    bb = np.array((1,4*n), dtype=np.double)
+    bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4))
+    PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA)
+    return bb
+
+def frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ):
+    cdef siz n = bb.shape[0]
+    Rs = RLEs(n)
+    rleFrBbox( <RLE*> Rs._R, <const BB> bb.data, h, w, n )
+    objs = _toString(Rs)
+    return objs
+
+def frPoly( poly, siz h, siz w ):
+    cdef np.ndarray[np.double_t, ndim=1] np_poly
+    n = len(poly)
+    Rs = RLEs(n)
+    for i, p in enumerate(poly):
+        np_poly = np.array(p, dtype=np.double, order='F')
+        rleFrPoly( <RLE*>&Rs._R[i], <const double*> np_poly.data, len(np_poly)/2, h, w )
+    objs = _toString(Rs)
+    return objs
+
+def frUncompressedRLE(ucRles, siz h, siz w):
+    cdef np.ndarray[np.uint32_t, ndim=1] cnts
+    cdef RLE R
+    cdef uint *data
+    n = len(ucRles)
+    objs = []
+    for i in range(n):
+        Rs = RLEs(1)
+        cnts = np.array(ucRles[i]['counts'], dtype=np.uint32)
+        # time for malloc can be saved here but it's fine
+        data = <uint*> malloc(len(cnts)* sizeof(uint))
+        for j in range(len(cnts)):
+            data[j] = <uint> cnts[j]
+        R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), <uint*> data)
+        Rs._R[0] = R
+        objs.append(_toString(Rs)[0])
+    return objs
+
+def frPyObjects(pyobj, siz h, w):
+    if type(pyobj) == np.ndarray:
+        objs = frBbox(pyobj, h, w )
+    elif type(pyobj) == list and len(pyobj[0]) == 4:
+        objs = frBbox(pyobj, h, w )
+    elif type(pyobj) == list and len(pyobj[0]) > 4:
+        objs = frPoly(pyobj, h, w )
+    elif type(pyobj) == list and type(pyobj[0]) == dict:
+        objs = frUncompressedRLE(pyobj, h, w)
+    else:
+        raise Exception('input type is not supported.')
+    return objs
diff --git a/examples/rfcn/lib/pycocotools/coco.py b/examples/rfcn/lib/pycocotools/coco.py
new file mode 100644
index 000000000..5d9f6b826
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/coco.py
@@ -0,0 +1,351 @@
+__author__ = 'tylin'
+__version__ = '1.0.1'
+# Interface for accessing the Microsoft COCO dataset.
+
+# Microsoft COCO is a large image dataset designed for object detection,
+# segmentation, and caption generation. pycocotools is a Python API that
+# assists in loading, parsing and visualizing the annotations in COCO.
+# Please visit http://mscoco.org/ for more information on COCO, including
+# for the data, paper, and tutorials. The exact format of the annotations
+# is also described on the COCO website. For example usage of the pycocotools
+# please see pycocotools_demo.ipynb. In addition to this API, please download both
+# the COCO images and annotations in order to run the demo.
+
+# An alternative to using the API is to load the annotations directly
+# into Python dictionary
+# Using the API provides additional utility functions. Note that this API
+# supports both *instance* and *caption* annotations. In the case of
+# captions not all functions are defined (e.g. categories are undefined).
+
+# The following API functions are defined:
+#  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
+#  decodeMask - Decode binary mask M encoded via run-length encoding.
+#  encodeMask - Encode binary mask M using run-length encoding.
+#  getAnnIds  - Get ann ids that satisfy given filter conditions.
+#  getCatIds  - Get cat ids that satisfy given filter conditions.
+#  getImgIds  - Get img ids that satisfy given filter conditions.
+#  loadAnns   - Load anns with the specified ids.
+#  loadCats   - Load cats with the specified ids.
+#  loadImgs   - Load imgs with the specified ids.
+#  segToMask  - Convert polygon segmentation to binary mask.
+#  showAnns   - Display the specified annotations.
+#  loadRes    - Load algorithm results and create API for accessing them.
+#  download   - Download COCO images from mscoco.org server.
+# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
+# Help on each functions can be accessed by: "help COCO>function".
+
+# See also COCO>decodeMask,
+# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
+# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
+# COCO>loadImgs, COCO>segToMask, COCO>showAnns
+
+# Microsoft COCO Toolbox.      version 2.0
+# Data, paper, and tutorials available at:  http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
+# Licensed under the Simplified BSD License [see bsd.txt]
+
+import json
+import datetime
+import time
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+import numpy as np
+from skimage.draw import polygon
+import urllib
+import copy
+import itertools
+import mask
+import os
+
+class COCO:
+    def __init__(self, annotation_file=None):
+        """
+        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
+        :param annotation_file (str): location of annotation file
+        :param image_folder (str): location to the folder that hosts images.
+        :return:
+        """
+        # load dataset
+        self.dataset = {}
+        self.anns = []
+        self.imgToAnns = {}
+        self.catToImgs = {}
+        self.imgs = {}
+        self.cats = {}
+        if not annotation_file == None:
+            print 'loading annotations into memory...'
+            tic = time.time()
+            dataset = json.load(open(annotation_file, 'r'))
+            print 'Done (t=%0.2fs)'%(time.time()- tic)
+            self.dataset = dataset
+            self.createIndex()
+
+    def createIndex(self):
+        # create index
+        print 'creating index...'
+        anns = {}
+        imgToAnns = {}
+        catToImgs = {}
+        cats = {}
+        imgs = {}
+        if 'annotations' in self.dataset:
+            imgToAnns = {ann['image_id']: [] for ann in self.dataset['annotations']}
+            anns =      {ann['id']:       [] for ann in self.dataset['annotations']}
+            for ann in self.dataset['annotations']:
+                imgToAnns[ann['image_id']] += [ann]
+                anns[ann['id']] = ann
+
+        if 'images' in self.dataset:
+            imgs      = {im['id']: {} for im in self.dataset['images']}
+            for img in self.dataset['images']:
+                imgs[img['id']] = img
+
+        if 'categories' in self.dataset:
+            cats = {cat['id']: [] for cat in self.dataset['categories']}
+            for cat in self.dataset['categories']:
+                cats[cat['id']] = cat
+            catToImgs = {cat['id']: [] for cat in self.dataset['categories']}
+            if 'annotations' in self.dataset:
+                for ann in self.dataset['annotations']:
+                    catToImgs[ann['category_id']] += [ann['image_id']]
+
+        print 'index created!'
+
+        # create class members
+        self.anns = anns
+        self.imgToAnns = imgToAnns
+        self.catToImgs = catToImgs
+        self.imgs = imgs
+        self.cats = cats
+
+    def info(self):
+        """
+        Print information about the annotation file.
+        :return:
+        """
+        for key, value in self.dataset['info'].items():
+            print '%s: %s'%(key, value)
+
+    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
+        """
+        Get ann ids that satisfy given filter conditions. default skips that filter
+        :param imgIds  (int array)     : get anns for given imgs
+               catIds  (int array)     : get anns for given cats
+               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
+               iscrowd (boolean)       : get anns for given crowd label (False or True)
+        :return: ids (int array)       : integer array of ann ids
+        """
+        imgIds = imgIds if type(imgIds) == list else [imgIds]
+        catIds = catIds if type(catIds) == list else [catIds]
+
+        if len(imgIds) == len(catIds) == len(areaRng) == 0:
+            anns = self.dataset['annotations']
+        else:
+            if not len(imgIds) == 0:
+                # this can be changed by defaultdict
+                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                anns = list(itertools.chain.from_iterable(lists))
+            else:
+                anns = self.dataset['annotations']
+            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
+            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
+        if not iscrowd == None:
+            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+        else:
+            ids = [ann['id'] for ann in anns]
+        return ids
+
+    def getCatIds(self, catNms=[], supNms=[], catIds=[]):
+        """
+        filtering parameters. default skips that filter.
+        :param catNms (str array)  : get cats for given cat names
+        :param supNms (str array)  : get cats for given supercategory names
+        :param catIds (int array)  : get cats for given cat ids
+        :return: ids (int array)   : integer array of cat ids
+        """
+        catNms = catNms if type(catNms) == list else [catNms]
+        supNms = supNms if type(supNms) == list else [supNms]
+        catIds = catIds if type(catIds) == list else [catIds]
+
+        if len(catNms) == len(supNms) == len(catIds) == 0:
+            cats = self.dataset['categories']
+        else:
+            cats = self.dataset['categories']
+            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
+            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
+            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
+        ids = [cat['id'] for cat in cats]
+        return ids
+
+    def getImgIds(self, imgIds=[], catIds=[]):
+        '''
+        Get img ids that satisfy given filter conditions.
+        :param imgIds (int array) : get imgs for given ids
+        :param catIds (int array) : get imgs with all given cats
+        :return: ids (int array)  : integer array of img ids
+        '''
+        imgIds = imgIds if type(imgIds) == list else [imgIds]
+        catIds = catIds if type(catIds) == list else [catIds]
+
+        if len(imgIds) == len(catIds) == 0:
+            ids = self.imgs.keys()
+        else:
+            ids = set(imgIds)
+            for i, catId in enumerate(catIds):
+                if i == 0 and len(ids) == 0:
+                    ids = set(self.catToImgs[catId])
+                else:
+                    ids &= set(self.catToImgs[catId])
+        return list(ids)
+
+    def loadAnns(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying anns
+        :return: anns (object array) : loaded ann objects
+        """
+        if type(ids) == list:
+            return [self.anns[id] for id in ids]
+        elif type(ids) == int:
+            return [self.anns[ids]]
+
+    def loadCats(self, ids=[]):
+        """
+        Load cats with the specified ids.
+        :param ids (int array)       : integer ids specifying cats
+        :return: cats (object array) : loaded cat objects
+        """
+        if type(ids) == list:
+            return [self.cats[id] for id in ids]
+        elif type(ids) == int:
+            return [self.cats[ids]]
+
+    def loadImgs(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying img
+        :return: imgs (object array) : loaded img objects
+        """
+        if type(ids) == list:
+            return [self.imgs[id] for id in ids]
+        elif type(ids) == int:
+            return [self.imgs[ids]]
+
+    def showAnns(self, anns):
+        """
+        Display the specified annotations.
+        :param anns (array of object): annotations to display
+        :return: None
+        """
+        if len(anns) == 0:
+            return 0
+        if 'segmentation' in anns[0]:
+            datasetType = 'instances'
+        elif 'caption' in anns[0]:
+            datasetType = 'captions'
+        if datasetType == 'instances':
+            ax = plt.gca()
+            polygons = []
+            color = []
+            for ann in anns:
+                c = np.random.random((1, 3)).tolist()[0]
+                if type(ann['segmentation']) == list:
+                    # polygon
+                    for seg in ann['segmentation']:
+                        poly = np.array(seg).reshape((len(seg)/2, 2))
+                        polygons.append(Polygon(poly, True,alpha=0.4))
+                        color.append(c)
+                else:
+                    # mask
+                    t = self.imgs[ann['image_id']]
+                    if type(ann['segmentation']['counts']) == list:
+                        rle = mask.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                    else:
+                        rle = [ann['segmentation']]
+                    m = mask.decode(rle)
+                    img = np.ones( (m.shape[0], m.shape[1], 3) )
+                    if ann['iscrowd'] == 1:
+                        color_mask = np.array([2.0,166.0,101.0])/255
+                    if ann['iscrowd'] == 0:
+                        color_mask = np.random.random((1, 3)).tolist()[0]
+                    for i in range(3):
+                        img[:,:,i] = color_mask[i]
+                    ax.imshow(np.dstack( (img, m*0.5) ))
+            p = PatchCollection(polygons, facecolors=color, edgecolors=(0,0,0,1), linewidths=3, alpha=0.4)
+            ax.add_collection(p)
+        elif datasetType == 'captions':
+            for ann in anns:
+                print ann['caption']
+
+    def loadRes(self, resFile):
+        """
+        Load result file and return a result api object.
+        :param   resFile (str)     : file name of result file
+        :return: res (obj)         : result api object
+        """
+        res = COCO()
+        res.dataset['images'] = [img for img in self.dataset['images']]
+        # res.dataset['info'] = copy.deepcopy(self.dataset['info'])
+        # res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses'])
+
+        print 'Loading and preparing results...     '
+        tic = time.time()
+        anns    = json.load(open(resFile))
+        assert type(anns) == list, 'results in not an array of objects'
+        annsImgIds = [ann['image_id'] for ann in anns]
+        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
+               'Results do not correspond to current coco set'
+        if 'caption' in anns[0]:
+            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
+            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+            for id, ann in enumerate(anns):
+                ann['id'] = id+1
+        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                bb = ann['bbox']
+                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
+                if not 'segmentation' in ann:
+                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann['area'] = bb[2]*bb[3]
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        elif 'segmentation' in anns[0]:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                # now only support compressed RLE format as segmentation results
+                ann['area'] = mask.area([ann['segmentation']])[0]
+                if not 'bbox' in ann:
+                    ann['bbox'] = mask.toBbox([ann['segmentation']])[0]
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        print 'DONE (t=%0.2fs)'%(time.time()- tic)
+
+        res.dataset['annotations'] = anns
+        res.createIndex()
+        return res
+
+    def download( self, tarDir = None, imgIds = [] ):
+        '''
+        Download COCO images from mscoco.org server.
+        :param tarDir (str): COCO results directory name
+               imgIds (list): images to be downloaded
+        :return:
+        '''
+        if tarDir is None:
+            print 'Please specify target directory'
+            return -1
+        if len(imgIds) == 0:
+            imgs = self.imgs.values()
+        else:
+            imgs = self.loadImgs(imgIds)
+        N = len(imgs)
+        if not os.path.exists(tarDir):
+            os.makedirs(tarDir)
+        for i, img in enumerate(imgs):
+            tic = time.time()
+            fname = os.path.join(tarDir, img['file_name'])
+            if not os.path.exists(fname):
+                urllib.urlretrieve(img['coco_url'], fname)
+            print 'downloaded %d/%d images (t=%.1fs)'%(i, N, time.time()- tic)
diff --git a/examples/rfcn/lib/pycocotools/cocoeval.py b/examples/rfcn/lib/pycocotools/cocoeval.py
new file mode 100644
index 000000000..f389eb0f2
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/cocoeval.py
@@ -0,0 +1,444 @@
+__author__ = 'tsungyi'
+
+import numpy as np
+import datetime
+import time
+from collections import defaultdict
+import mask
+import copy
+
+class COCOeval:
+    # Interface for evaluating detection on the Microsoft COCO dataset.
+    #
+    # The usage for CocoEval is as follows:
+    #  cocoGt=..., cocoDt=...       # load dataset and results
+    #  E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
+    #  E.params.recThrs = ...;      # set parameters as desired
+    #  E.evaluate();                # run per image evaluation
+    #  E.accumulate();              # accumulate per image results
+    #  E.summarize();               # display summary metrics of results
+    # For example usage see evalDemo.m and http://mscoco.org/.
+    #
+    # The evaluation parameters are as follows (defaults in brackets):
+    #  imgIds     - [all] N img ids to use for evaluation
+    #  catIds     - [all] K cat ids to use for evaluation
+    #  iouThrs    - [.5:.05:.95] T=10 IoU thresholds for evaluation
+    #  recThrs    - [0:.01:1] R=101 recall thresholds for evaluation
+    #  areaRng    - [...] A=4 object area ranges for evaluation
+    #  maxDets    - [1 10 100] M=3 thresholds on max detections per image
+    #  useSegm    - [1] if true evaluate against ground-truth segments
+    #  useCats    - [1] if true use category labels for evaluation    # Note: if useSegm=0 the evaluation is run on bounding boxes.
+    # Note: if useCats=0 category labels are ignored as in proposal scoring.
+    # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
+    #
+    # evaluate(): evaluates detections on every image and every category and
+    # concats the results into the "evalImgs" with fields:
+    #  dtIds      - [1xD] id for each of the D detections (dt)
+    #  gtIds      - [1xG] id for each of the G ground truths (gt)
+    #  dtMatches  - [TxD] matching gt id at each IoU or 0
+    #  gtMatches  - [TxG] matching dt id at each IoU or 0
+    #  dtScores   - [1xD] confidence of each dt
+    #  gtIgnore   - [1xG] ignore flag for each gt
+    #  dtIgnore   - [TxD] ignore flag for each dt at each IoU
+    #
+    # accumulate(): accumulates the per-image, per-category evaluation
+    # results in "evalImgs" into the dictionary "eval" with fields:
+    #  params     - parameters used for evaluation
+    #  date       - date evaluation was performed
+    #  counts     - [T,R,K,A,M] parameter dimensions (see above)
+    #  precision  - [TxRxKxAxM] precision for every evaluation setting
+    #  recall     - [TxKxAxM] max recall for every evaluation setting
+    # Note: precision and recall==-1 for settings with no gt objects.
+    #
+    # See also coco, mask, pycocoDemo, pycocoEvalDemo
+    #
+    # Microsoft COCO Toolbox.      version 2.0
+    # Data, paper, and tutorials available at:  http://mscoco.org/
+    # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+    # Licensed under the Simplified BSD License [see coco/license.txt]
+    def __init__(self, cocoGt=None, cocoDt=None):
+        '''
+        Initialize CocoEval using coco APIs for gt and dt
+        :param cocoGt: coco object with ground truth annotations
+        :param cocoDt: coco object with detection results
+        :return: None
+        '''
+        self.cocoGt   = cocoGt              # ground truth COCO API
+        self.cocoDt   = cocoDt              # detections COCO API
+        self.params   = {}                  # evaluation parameters
+        self.evalImgs = defaultdict(list)   # per-image per-category evaluation results [KxAxI] elements
+        self.eval     = {}                  # accumulated evaluation results
+        self._gts = defaultdict(list)       # gt for evaluation
+        self._dts = defaultdict(list)       # dt for evaluation
+        self.params = Params()              # parameters
+        self._paramsEval = {}               # parameters for evaluation
+        self.stats = []                     # result summarization
+        self.ious = {}                      # ious between all gts and dts
+        if not cocoGt is None:
+            self.params.imgIds = sorted(cocoGt.getImgIds())
+            self.params.catIds = sorted(cocoGt.getCatIds())
+
+
+    def _prepare(self):
+        '''
+        Prepare ._gts and ._dts for evaluation based on params
+        :return: None
+        '''
+        #
+        def _toMask(objs, coco):
+            # modify segmentation by reference
+            for obj in objs:
+                t = coco.imgs[obj['image_id']]
+                if type(obj['segmentation']) == list:
+                    if type(obj['segmentation'][0]) == dict:
+                        print 'debug'
+                    obj['segmentation'] = mask.frPyObjects(obj['segmentation'],t['height'],t['width'])
+                    if len(obj['segmentation']) == 1:
+                        obj['segmentation'] = obj['segmentation'][0]
+                    else:
+                        # an object can have multiple polygon regions
+                        # merge them into one RLE mask
+                        obj['segmentation'] = mask.merge(obj['segmentation'])
+                elif type(obj['segmentation']) == dict and type(obj['segmentation']['counts']) == list:
+                    obj['segmentation'] = mask.frPyObjects([obj['segmentation']],t['height'],t['width'])[0]
+                elif type(obj['segmentation']) == dict and \
+                     type(obj['segmentation']['counts'] == unicode or type(obj['segmentation']['counts']) == str):
+                    pass
+                else:
+                    raise Exception('segmentation format not supported.')
+        p = self.params
+        if p.useCats:
+            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
+            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
+        else:
+            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
+            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
+
+        if p.useSegm:
+            _toMask(gts, self.cocoGt)
+            _toMask(dts, self.cocoDt)
+        self._gts = defaultdict(list)       # gt for evaluation
+        self._dts = defaultdict(list)       # dt for evaluation
+        for gt in gts:
+            self._gts[gt['image_id'], gt['category_id']].append(gt)
+        for dt in dts:
+            self._dts[dt['image_id'], dt['category_id']].append(dt)
+        self.evalImgs = defaultdict(list)   # per-image per-category evaluation results
+        self.eval     = {}                  # accumulated evaluation results
+
+    def evaluate(self):
+        '''
+        Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
+        :return: None
+        '''
+        tic = time.time()
+        print 'Running per image evaluation...      '
+        p = self.params
+        p.imgIds = list(np.unique(p.imgIds))
+        if p.useCats:
+            p.catIds = list(np.unique(p.catIds))
+        p.maxDets = sorted(p.maxDets)
+        self.params=p
+
+        self._prepare()
+        # loop through images, area range, max detection number
+        catIds = p.catIds if p.useCats else [-1]
+
+        computeIoU = self.computeIoU
+        self.ious = {(imgId, catId): computeIoU(imgId, catId) \
+                        for imgId in p.imgIds
+                        for catId in catIds}
+
+        evaluateImg = self.evaluateImg
+        maxDet = p.maxDets[-1]
+        self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)
+                 for catId in catIds
+                 for areaRng in p.areaRng
+                 for imgId in p.imgIds
+             ]
+        self._paramsEval = copy.deepcopy(self.params)
+        toc = time.time()
+        print 'DONE (t=%0.2fs).'%(toc-tic)
+
+    def computeIoU(self, imgId, catId):
+        p = self.params
+        if p.useCats:
+            gt = self._gts[imgId,catId]
+            dt = self._dts[imgId,catId]
+        else:
+            gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
+            dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
+        if len(gt) == 0 and len(dt) ==0:
+            return []
+        dt = sorted(dt, key=lambda x: -x['score'])
+        if len(dt) > p.maxDets[-1]:
+            dt=dt[0:p.maxDets[-1]]
+
+        if p.useSegm:
+            g = [g['segmentation'] for g in gt]
+            d = [d['segmentation'] for d in dt]
+        else:
+            g = [g['bbox'] for g in gt]
+            d = [d['bbox'] for d in dt]
+
+        # compute iou between each dt and gt region
+        iscrowd = [int(o['iscrowd']) for o in gt]
+        ious = mask.iou(d,g,iscrowd)
+        return ious
+
+    def evaluateImg(self, imgId, catId, aRng, maxDet):
+        '''
+        perform evaluation for single category and image
+        :return: dict (single image results)
+        '''
+        #
+        p = self.params
+        if p.useCats:
+            gt = self._gts[imgId,catId]
+            dt = self._dts[imgId,catId]
+        else:
+            gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
+            dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
+        if len(gt) == 0 and len(dt) ==0:
+            return None
+
+        for g in gt:
+            if 'ignore' not in g:
+                g['ignore'] = 0
+            if g['iscrowd'] == 1 or g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]):
+                g['_ignore'] = 1
+            else:
+                g['_ignore'] = 0
+
+        # sort dt highest score first, sort gt ignore last
+        # gt = sorted(gt, key=lambda x: x['_ignore'])
+        gtind = [ind for (ind, g) in sorted(enumerate(gt), key=lambda (ind, g): g['_ignore']) ]
+
+        gt = [gt[ind] for ind in gtind]
+        dt = sorted(dt, key=lambda x: -x['score'])[0:maxDet]
+        iscrowd = [int(o['iscrowd']) for o in gt]
+        # load computed ious
+        N_iou = len(self.ious[imgId, catId])
+        ious = self.ious[imgId, catId][0:maxDet, np.array(gtind)] if N_iou >0 else self.ious[imgId, catId]
+
+        T = len(p.iouThrs)
+        G = len(gt)
+        D = len(dt)
+        gtm  = np.zeros((T,G))
+        dtm  = np.zeros((T,D))
+        gtIg = np.array([g['_ignore'] for g in gt])
+        dtIg = np.zeros((T,D))
+        if not len(ious)==0:
+            for tind, t in enumerate(p.iouThrs):
+                for dind, d in enumerate(dt):
+                    # information about best match so far (m=-1 -> unmatched)
+                    iou = min([t,1-1e-10])
+                    m   = -1
+                    for gind, g in enumerate(gt):
+                        # if this gt already matched, and not a crowd, continue
+                        if gtm[tind,gind]>0 and not iscrowd[gind]:
+                            continue
+                        # if dt matched to reg gt, and on ignore gt, stop
+                        if m>-1 and gtIg[m]==0 and gtIg[gind]==1:
+                            break
+                        # continue to next gt unless better match made
+                        if ious[dind,gind] < iou:
+                            continue
+                        # match successful and best so far, store appropriately
+                        iou=ious[dind,gind]
+                        m=gind
+                    # if match made store id of match for both dt and gt
+                    if m ==-1:
+                        continue
+                    dtIg[tind,dind] = gtIg[m]
+                    dtm[tind,dind]  = gt[m]['id']
+                    gtm[tind,m]     = d['id']
+        # set unmatched detections outside of area range to ignore
+        a = np.array([d['area']<aRng[0] or d['area']>aRng[1] for d in dt]).reshape((1, len(dt)))
+        dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0)))
+        # store results for given image and category
+        return {
+                'image_id':     imgId,
+                'category_id':  catId,
+                'aRng':         aRng,
+                'maxDet':       maxDet,
+                'dtIds':        [d['id'] for d in dt],
+                'gtIds':        [g['id'] for g in gt],
+                'dtMatches':    dtm,
+                'gtMatches':    gtm,
+                'dtScores':     [d['score'] for d in dt],
+                'gtIgnore':     gtIg,
+                'dtIgnore':     dtIg,
+            }
+
+    def accumulate(self, p = None):
+        '''
+        Accumulate per image evaluation results and store the result in self.eval
+        :param p: input params for evaluation
+        :return: None
+        '''
+        print 'Accumulating evaluation results...   '
+        tic = time.time()
+        if not self.evalImgs:
+            print 'Please run evaluate() first'
+        # allows input customized parameters
+        if p is None:
+            p = self.params
+        p.catIds = p.catIds if p.useCats == 1 else [-1]
+        T           = len(p.iouThrs)
+        R           = len(p.recThrs)
+        K           = len(p.catIds) if p.useCats else 1
+        A           = len(p.areaRng)
+        M           = len(p.maxDets)
+        precision   = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
+        recall      = -np.ones((T,K,A,M))
+
+        # create dictionary for future indexing
+        _pe = self._paramsEval
+        catIds = _pe.catIds if _pe.useCats else [-1]
+        setK = set(catIds)
+        setA = set(map(tuple, _pe.areaRng))
+        setM = set(_pe.maxDets)
+        setI = set(_pe.imgIds)
+        # get inds to evaluate
+        k_list = [n for n, k in enumerate(p.catIds)  if k in setK]
+        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
+        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
+        i_list = [n for n, i in enumerate(p.imgIds)  if i in setI]
+        # K0 = len(_pe.catIds)
+        I0 = len(_pe.imgIds)
+        A0 = len(_pe.areaRng)
+        # retrieve E at each category, area range, and max number of detections
+        for k, k0 in enumerate(k_list):
+            Nk = k0*A0*I0
+            for a, a0 in enumerate(a_list):
+                Na = a0*I0
+                for m, maxDet in enumerate(m_list):
+                    E = [self.evalImgs[Nk+Na+i] for i in i_list]
+                    E = filter(None, E)
+                    if len(E) == 0:
+                        continue
+                    dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
+
+                    # different sorting method generates slightly different results.
+                    # mergesort is used to be consistent as Matlab implementation.
+                    inds = np.argsort(-dtScores, kind='mergesort')
+
+                    dtm  = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
+                    dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet]  for e in E], axis=1)[:,inds]
+                    gtIg = np.concatenate([e['gtIgnore']  for e in E])
+                    npig = len([ig for ig in gtIg if ig == 0])
+                    if npig == 0:
+                        continue
+                    tps = np.logical_and(               dtm,  np.logical_not(dtIg) )
+                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
+
+                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
+                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
+                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
+                        tp = np.array(tp)
+                        fp = np.array(fp)
+                        nd = len(tp)
+                        rc = tp / npig
+                        pr = tp / (fp+tp+np.spacing(1))
+                        q  = np.zeros((R,))
+
+                        if nd:
+                            recall[t,k,a,m] = rc[-1]
+                        else:
+                            recall[t,k,a,m] = 0
+
+                        # numpy is slow without cython optimization for accessing elements
+                        # use python array gets significant speed improvement
+                        pr = pr.tolist(); q = q.tolist()
+
+                        for i in range(nd-1, 0, -1):
+                            if pr[i] > pr[i-1]:
+                                pr[i-1] = pr[i]
+
+                        inds = np.searchsorted(rc, p.recThrs)
+                        try:
+                            for ri, pi in enumerate(inds):
+                                q[ri] = pr[pi]
+                        except:
+                            pass
+                        precision[t,:,k,a,m] = np.array(q)
+        self.eval = {
+            'params': p,
+            'counts': [T, R, K, A, M],
+            'date': datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            'precision': precision,
+            'recall':   recall,
+        }
+        toc = time.time()
+        print 'DONE (t=%0.2fs).'%( toc-tic )
+
+    def summarize(self):
+        '''
+        Compute and display summary metrics for evaluation results.
+        Note this functin can *only* be applied on the default parameter setting
+        '''
+        def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
+            p = self.params
+            iStr        = ' {:<18} {} @[ IoU={:<9} | area={:>6} | maxDets={:>3} ] = {}'
+            titleStr    = 'Average Precision' if ap == 1 else 'Average Recall'
+            typeStr     = '(AP)' if ap==1 else '(AR)'
+            iouStr      = '%0.2f:%0.2f'%(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else '%0.2f'%(iouThr)
+            areaStr     = areaRng
+            maxDetsStr  = '%d'%(maxDets)
+
+            aind = [i for i, aRng in enumerate(['all', 'small', 'medium', 'large']) if aRng == areaRng]
+            mind = [i for i, mDet in enumerate([1, 10, 100]) if mDet == maxDets]
+            if ap == 1:
+                # dimension of precision: [TxRxKxAxM]
+                s = self.eval['precision']
+                # IoU
+                if iouThr is not None:
+                    t = np.where(iouThr == p.iouThrs)[0]
+                    s = s[t]
+                # areaRng
+                s = s[:,:,:,aind,mind]
+            else:
+                # dimension of recall: [TxKxAxM]
+                s = self.eval['recall']
+                s = s[:,:,aind,mind]
+            if len(s[s>-1])==0:
+                mean_s = -1
+            else:
+                mean_s = np.mean(s[s>-1])
+            print iStr.format(titleStr, typeStr, iouStr, areaStr, maxDetsStr, '%.3f'%(float(mean_s)))
+            return mean_s
+
+        if not self.eval:
+            raise Exception('Please run accumulate() first')
+        self.stats = np.zeros((12,))
+        self.stats[0] = _summarize(1)
+        self.stats[1] = _summarize(1,iouThr=.5)
+        self.stats[2] = _summarize(1,iouThr=.75)
+        self.stats[3] = _summarize(1,areaRng='small')
+        self.stats[4] = _summarize(1,areaRng='medium')
+        self.stats[5] = _summarize(1,areaRng='large')
+        self.stats[6] = _summarize(0,maxDets=1)
+        self.stats[7] = _summarize(0,maxDets=10)
+        self.stats[8] = _summarize(0,maxDets=100)
+        self.stats[9]  = _summarize(0,areaRng='small')
+        self.stats[10] = _summarize(0,areaRng='medium')
+        self.stats[11] = _summarize(0,areaRng='large')
+
+    def __str__(self):
+        self.summarize()
+
+class Params:
+    '''
+    Params for coco evaluation api
+    '''
+    def __init__(self):
+        self.imgIds = []
+        self.catIds = []
+        # np.arange causes trouble.  the data point on arange is slightly larger than the true value
+        self.iouThrs = np.linspace(.5, 0.95, np.round((0.95-.5)/.05)+1, endpoint=True)
+        self.recThrs = np.linspace(.0, 1.00, np.round((1.00-.0)/.01)+1, endpoint=True)
+        self.maxDets = [1,10,100]
+        self.areaRng = [ [0**2,1e5**2], [0**2, 32**2], [32**2, 96**2], [96**2, 1e5**2] ]
+        self.useSegm = 0
+        self.useCats = 1
\ No newline at end of file
diff --git a/examples/rfcn/lib/pycocotools/license.txt b/examples/rfcn/lib/pycocotools/license.txt
new file mode 100644
index 000000000..495c163a6
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/license.txt
@@ -0,0 +1,26 @@
+Copyright (c) 2014, Piotr Dollar and Tsung-Yi Lin
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met: 
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer. 
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution. 
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+The views and conclusions contained in the software and documentation are those
+of the authors and should not be interpreted as representing official policies, 
+either expressed or implied, of the FreeBSD Project.
diff --git a/examples/rfcn/lib/pycocotools/mask.py b/examples/rfcn/lib/pycocotools/mask.py
new file mode 100644
index 000000000..673254674
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/mask.py
@@ -0,0 +1,82 @@
+__author__ = 'tsungyi'
+
+import pycocotools._mask as _mask
+
+# Interface for manipulating masks stored in RLE format.
+#
+# RLE is a simple yet efficient format for storing binary masks. RLE
+# first divides a vector (or vectorized image) into a series of piecewise
+# constant regions and then for each piece simply stores the length of
+# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would
+# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1]
+# (note that the odd counts are always the numbers of zeros). Instead of
+# storing the counts directly, additional compression is achieved with a
+# variable bitrate representation based on a common scheme called LEB128.
+#
+# Compression is greatest given large piecewise constant regions.
+# Specifically, the size of the RLE is proportional to the number of
+# *boundaries* in M (or for an image the number of boundaries in the y
+# direction). Assuming fairly simple shapes, the RLE representation is
+# O(sqrt(n)) where n is number of pixels in the object. Hence space usage
+# is substantially lower, especially for large simple objects (large n).
+#
+# Many common operations on masks can be computed directly using the RLE
+# (without need for decoding). This includes computations such as area,
+# union, intersection, etc. All of these operations are linear in the
+# size of the RLE, in other words they are O(sqrt(n)) where n is the area
+# of the object. Computing these operations on the original mask is O(n).
+# Thus, using the RLE can result in substantial computational savings.
+#
+# The following API functions are defined:
+#  encode         - Encode binary masks using RLE.
+#  decode         - Decode binary masks encoded via RLE.
+#  merge          - Compute union or intersection of encoded masks.
+#  iou            - Compute intersection over union between masks.
+#  area           - Compute area of encoded masks.
+#  toBbox         - Get bounding boxes surrounding encoded masks.
+#  frPyObjects    - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask.
+#
+# Usage:
+#  Rs     = encode( masks )
+#  masks  = decode( Rs )
+#  R      = merge( Rs, intersect=false )
+#  o      = iou( dt, gt, iscrowd )
+#  a      = area( Rs )
+#  bbs    = toBbox( Rs )
+#  Rs     = frPyObjects( [pyObjects], h, w )
+#
+# In the API the following formats are used:
+#  Rs      - [dict] Run-length encoding of binary masks
+#  R       - dict Run-length encoding of binary mask
+#  masks   - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order)
+#  iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore
+#  bbs     - [nx4] Bounding box(es) stored as [x y w h]
+#  poly    - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list)
+#  dt,gt   - May be either bounding boxes or encoded masks
+# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel).
+#
+# Finally, a note about the intersection over union (iou) computation.
+# The standard iou of a ground truth (gt) and detected (dt) object is
+#  iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt))
+# For "crowd" regions, we use a modified criteria. If a gt object is
+# marked as "iscrowd", we allow a dt to match any subregion of the gt.
+# Choosing gt' in the crowd gt that best matches the dt can be done using
+# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing
+#  iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt)
+# For crowd gt regions we use this modified criteria above for the iou.
+#
+# To compile run "python setup.py build_ext --inplace"
+# Please do not contact us for help with compiling.
+#
+# Microsoft COCO Toolbox.      version 2.0
+# Data, paper, and tutorials available at:  http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+# Licensed under the Simplified BSD License [see coco/license.txt]
+
+encode      = _mask.encode
+decode      = _mask.decode
+iou         = _mask.iou
+merge       = _mask.merge
+area        = _mask.area
+toBbox      = _mask.toBbox
+frPyObjects = _mask.frPyObjects
\ No newline at end of file
diff --git a/examples/rfcn/lib/pycocotools/maskApi.c b/examples/rfcn/lib/pycocotools/maskApi.c
new file mode 100644
index 000000000..2b2d89116
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/maskApi.c
@@ -0,0 +1,208 @@
+/**************************************************************************
+* Microsoft COCO Toolbox.      version 2.0
+* Data, paper, and tutorials available at:  http://mscoco.org/
+* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+* Licensed under the Simplified BSD License [see coco/license.txt]
+**************************************************************************/
+#include "maskApi.h"
+#include <math.h>
+#include <stdlib.h>
+
+uint umin( uint a, uint b ) { return (a<b) ? a : b; }
+uint umax( uint a, uint b ) { return (a>b) ? a : b; }
+
+void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) {
+  R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m);
+  if(cnts) for(siz j=0; j<m; j++) R->cnts[j]=cnts[j];
+}
+
+void rleFree( RLE *R ) {
+  free(R->cnts); R->cnts=0;
+}
+
+void rlesInit( RLE **R, siz n ) {
+  *R = (RLE*) malloc(sizeof(RLE)*n);
+  for(siz i=0; i<n; i++) rleInit((*R)+i,0,0,0,0);
+}
+
+void rlesFree( RLE **R, siz n ) {
+  for(siz i=0; i<n; i++) rleFree((*R)+i); free(*R); *R=0;
+}
+
+void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n ) {
+  siz i, j, k, a=w*h; uint c, *cnts; byte p;
+  cnts = malloc(sizeof(uint)*(a+1));
+  for(i=0; i<n; i++) {
+    const byte *T=M+a*i; k=0; p=0; c=0;
+    for(j=0; j<a; j++) { if(T[j]!=p) { cnts[k++]=c; c=0; p=T[j]; } c++; }
+    cnts[k++]=c; rleInit(R+i,h,w,k,cnts);
+  }
+  free(cnts);
+}
+
+void rleDecode( const RLE *R, byte *M, siz n ) {
+  for( siz i=0; i<n; i++ ) {
+    byte v=0; for( siz j=0; j<R[i].m; j++ ) {
+      for( siz k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}
+}
+
+void rleMerge( const RLE *R, RLE *M, siz n, bool intersect ) {
+  uint *cnts, c, ca, cb, cc, ct; bool v, va, vb, vp;
+  siz i, a, b, h=R[0].h, w=R[0].w, m=R[0].m; RLE A, B;
+  if(n==0) { rleInit(M,0,0,0,0); return; }
+  if(n==1) { rleInit(M,h,w,m,R[0].cnts); return; }
+  cnts = malloc(sizeof(uint)*(h*w+1));
+  for( a=0; a<m; a++ ) cnts[a]=R[0].cnts[a];
+  for( i=1; i<n; i++ ) {
+    B=R[i]; if(B.h!=h||B.w!=w) { h=w=m=0; break; }
+    rleInit(&A,h,w,m,cnts); ca=A.cnts[0]; cb=B.cnts[0];
+    v=va=vb=0; m=0; a=b=1; cc=0; ct=1;
+    while( ct>0 ) {
+      c=umin(ca,cb); cc+=c; ct=0;
+      ca-=c; if(!ca && a<A.m) { ca=A.cnts[a++]; va=!va; } ct+=ca;
+      cb-=c; if(!cb && b<B.m) { cb=B.cnts[b++]; vb=!vb; } ct+=cb;
+      vp=v; if(intersect) v=va&&vb; else v=va||vb;
+      if( v!=vp||ct==0 ) { cnts[m++]=cc; cc=0; }
+    }
+    rleFree(&A);
+  }
+  rleInit(M,h,w,m,cnts); free(cnts);
+}
+
+void rleArea( const RLE *R, siz n, uint *a ) {
+  for( siz i=0; i<n; i++ ) {
+    a[i]=0; for( siz j=1; j<R[i].m; j+=2 ) a[i]+=R[i].cnts[j]; }
+}
+
+void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ) {
+  siz g, d; BB db, gb; bool crowd;
+  db=malloc(sizeof(double)*m*4); rleToBbox(dt,db,m);
+  gb=malloc(sizeof(double)*n*4); rleToBbox(gt,gb,n);
+  bbIou(db,gb,m,n,iscrowd,o); free(db); free(gb);
+  for( g=0; g<n; g++ ) for( d=0; d<m; d++ ) if(o[g*m+d]>0) {
+    crowd=iscrowd!=NULL && iscrowd[g];
+    if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; }
+    siz ka, kb, a, b; uint c, ca, cb, ct, i, u; bool va, vb;
+    ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0;
+    cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1;
+    while( ct>0 ) {
+      c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0;
+      ca-=c; if(!ca && a<ka) { ca=dt[d].cnts[a++]; va=!va; } ct+=ca;
+      cb-=c; if(!cb && b<kb) { cb=gt[g].cnts[b++]; vb=!vb; } ct+=cb;
+    }
+    if(i==0) u=1; else if(crowd) rleArea(dt+d,1,&u);
+    o[g*m+d] = (double)i/(double)u;
+  }
+}
+
+void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) {
+  double h, w, i, u, ga, da; siz g, d; bool crowd;
+  for( g=0; g<n; g++ ) {
+    BB G=gt+g*4; ga=G[2]*G[3]; crowd=iscrowd!=NULL && iscrowd[g];
+    for( d=0; d<m; d++ ) {
+      BB D=dt+d*4; da=D[2]*D[3]; o[g*m+d]=0;
+      w=fmin(D[2]+D[0],G[2]+G[0])-fmax(D[0],G[0]); if(w<=0) continue;
+      h=fmin(D[3]+D[1],G[3]+G[1])-fmax(D[1],G[1]); if(h<=0) continue;
+      i=w*h; u = crowd ? da : da+ga-i; o[g*m+d]=i/u;
+    }
+  }
+}
+
+void rleToBbox( const RLE *R, BB bb, siz n ) {
+  for( siz i=0; i<n; i++ ) {
+    uint h, w, x, y, xs, ys, xe, ye, cc, t; siz j, m;
+    h=(uint)R[i].h; w=(uint)R[i].w; m=R[i].m;
+    m=((siz)(m/2))*2; xs=w; ys=h; xe=ye=0; cc=0;
+    if(m==0) { bb[4*i+0]=bb[4*i+1]=bb[4*i+2]=bb[4*i+3]=0; continue; }
+    for( j=0; j<m; j++ ) {
+      cc+=R[i].cnts[j]; t=cc-j%2; y=t%h; x=(t-y)/h;
+      xs=umin(xs,x); xe=umax(xe,x); ys=umin(ys,y); ye=umax(ye,y);
+    }
+    bb[4*i+0]=xs; bb[4*i+2]=xe-xs+1;
+    bb[4*i+1]=ys; bb[4*i+3]=ye-ys+1;
+  }
+}
+
+void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ) {
+  for( siz i=0; i<n; i++ ) {
+    double xs=bb[4*i+0], xe=xs+bb[4*i+2];
+    double ys=bb[4*i+1], ye=ys+bb[4*i+3];
+    double xy[8] = {xs,ys,xs,ye,xe,ye,xe,ys};
+    rleFrPoly( R+i, xy, 4, h, w );
+  }
+}
+
+int uintCompare(const void *a, const void *b) {
+  uint c=*((uint*)a), d=*((uint*)b); return c>d?1:c<d?-1:0;
+}
+
+void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ) {
+  // upsample and get discrete points densely along entire boundary
+  siz j, m=0; double scale=5; int *x, *y, *u, *v; uint *a, *b;
+  x=malloc(sizeof(int)*(k+1)); y=malloc(sizeof(int)*(k+1));
+  for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];
+  for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];
+  for(j=0; j<k; j++) m+=umax(abs(x[j]-x[j+1]),abs(y[j]-y[j+1]))+1;
+  u=malloc(sizeof(int)*m); v=malloc(sizeof(int)*m); m=0;
+  for( j=0; j<k; j++ ) {
+    int xs=x[j], xe=x[j+1], ys=y[j], ye=y[j+1], dx, dy, t;
+    bool flip; double s; dx=abs(xe-xs); dy=abs(ys-ye);
+    flip = (dx>=dy && xs>xe) || (dx<dy && ys>ye);
+    if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; }
+    s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy;
+    if(dx>=dy) for( int d=0; d<=dx; d++ ) {
+      t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++;
+    } else for( int d=0; d<=dy; d++ ) {
+      t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++;
+    }
+  }
+  // get points along y-boundary and downsample
+  free(x); free(y); k=m; m=0; double xd, yd;
+  x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k);
+  for( j=1; j<k; j++ ) if(u[j]!=u[j-1]) {
+    xd=(double)(u[j]<u[j-1]?u[j]:u[j]-1); xd=(xd+.5)/scale-.5;
+    if( floor(xd)!=xd || xd<0 || xd>w-1 ) continue;
+    yd=(double)(v[j]<v[j-1]?v[j]:v[j-1]); yd=(yd+.5)/scale-.5;
+    if(yd<0) yd=0; else if(yd>h) yd=h; yd=ceil(yd);
+    x[m]=(int) xd; y[m]=(int) yd; m++;
+  }
+  // compute rle encoding given y-boundary points
+  k=m; a=malloc(sizeof(uint)*(k+1));
+  for( j=0; j<k; j++ ) a[j]=(uint)(x[j]*(int)(h)+y[j]);
+  a[k++]=(uint)(h*w); free(u); free(v); free(x); free(y);
+  qsort(a,k,sizeof(uint),uintCompare); uint p=0;
+  for( j=0; j<k; j++ ) { uint t=a[j]; a[j]-=p; p=t; }
+  b=malloc(sizeof(uint)*k); j=m=0; b[m++]=a[j++];
+  while(j<k) if(a[j]>0) b[m++]=a[j++]; else {
+    j++; if(j<k) b[m-1]+=a[j++]; }
+  rleInit(R,h,w,m,b); free(a); free(b);
+}
+
+char* rleToString( const RLE *R ) {
+  // Similar to LEB128 but using 6 bits/char and ascii chars 48-111.
+  siz i, m=R->m, p=0; long x; bool more;
+  char *s=malloc(sizeof(char)*m*6);
+  for( i=0; i<m; i++ ) {
+    x=(long) R->cnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1;
+    while( more ) {
+      char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0;
+      if(more) c |= 0x20; c+=48; s[p++]=c;
+    }
+  }
+  s[p]=0; return s;
+}
+
+void rleFrString( RLE *R, char *s, siz h, siz w ) {
+  siz m=0, p=0, k; long x; bool more; uint *cnts;
+  while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;
+  while( s[p] ) {
+    x=0; k=0; more=1;
+    while( more ) {
+      char c=s[p]-48; x |= (c & 0x1f) << 5*k;
+      more = c & 0x20; p++; k++;
+      if(!more && (c & 0x10)) x |= -1 << 5*k;
+    }
+    if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;
+  }
+  rleInit(R,h,w,m,cnts); free(cnts);
+}
diff --git a/examples/rfcn/lib/pycocotools/maskApi.h b/examples/rfcn/lib/pycocotools/maskApi.h
new file mode 100644
index 000000000..ff16116c4
--- /dev/null
+++ b/examples/rfcn/lib/pycocotools/maskApi.h
@@ -0,0 +1,55 @@
+/**************************************************************************
+* Microsoft COCO Toolbox.      version 2.0
+* Data, paper, and tutorials available at:  http://mscoco.org/
+* Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
+* Licensed under the Simplified BSD License [see coco/license.txt]
+**************************************************************************/
+#pragma once
+#include <stdbool.h>
+
+typedef unsigned int uint;
+typedef unsigned long siz;
+typedef unsigned char byte;
+typedef double* BB;
+typedef struct { siz h, w, m; uint *cnts; } RLE;
+
+// Initialize/destroy RLE.
+void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts );
+void rleFree( RLE *R );
+
+// Initialize/destroy RLE array.
+void rlesInit( RLE **R, siz n );
+void rlesFree( RLE **R, siz n );
+
+// Encode binary masks using RLE.
+void rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n );
+
+// Decode binary masks encoded via RLE.
+void rleDecode( const RLE *R, byte *mask, siz n );
+
+// Compute union or intersection of encoded masks.
+void rleMerge( const RLE *R, RLE *M, siz n, bool intersect );
+
+// Compute area of encoded masks.
+void rleArea( const RLE *R, siz n, uint *a );
+
+// Compute intersection over union between masks.
+void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o );
+
+// Compute intersection over union between bounding boxes.
+void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o );
+
+// Get bounding boxes surrounding encoded masks.
+void rleToBbox( const RLE *R, BB bb, siz n );
+
+// Convert bounding boxes to encoded masks.
+void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n );
+
+// Convert polygon to encoded mask.
+void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w );
+
+// Get compressed string representation of encoded mask.
+char* rleToString( const RLE *R );
+
+// Convert from compressed string representation of encoded mask.
+void rleFrString( RLE *R, char *s, siz h, siz w );
diff --git a/examples/rfcn/lib/roi_data_layer/__init__.py b/examples/rfcn/lib/roi_data_layer/__init__.py
new file mode 100644
index 000000000..7ba6a65c5
--- /dev/null
+++ b/examples/rfcn/lib/roi_data_layer/__init__.py
@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
diff --git a/examples/rfcn/lib/roi_data_layer/layer.py b/examples/rfcn/lib/roi_data_layer/layer.py
new file mode 100644
index 000000000..ac591475c
--- /dev/null
+++ b/examples/rfcn/lib/roi_data_layer/layer.py
@@ -0,0 +1,201 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""The data layer used during training to train a Fast R-CNN network.
+
+RoIDataLayer implements a Caffe Python layer.
+"""
+
+import caffe
+from fast_rcnn.config import cfg
+from roi_data_layer.minibatch import get_minibatch
+import numpy as np
+import yaml
+from multiprocessing import Process, Queue
+
+class RoIDataLayer(caffe.Layer):
+    """Fast R-CNN data layer used for training."""
+
+    def _shuffle_roidb_inds(self):
+        """Randomly permute the training roidb."""
+        if cfg.TRAIN.ASPECT_GROUPING:
+            widths = np.array([r['width'] for r in self._roidb])
+            heights = np.array([r['height'] for r in self._roidb])
+            horz = (widths >= heights)
+            vert = np.logical_not(horz)
+            horz_inds = np.where(horz)[0]
+            vert_inds = np.where(vert)[0]
+            inds = np.hstack((
+                np.random.permutation(horz_inds),
+                np.random.permutation(vert_inds)))
+            inds = np.reshape(inds, (-1, 2))
+            row_perm = np.random.permutation(np.arange(inds.shape[0]))
+            inds = np.reshape(inds[row_perm, :], (-1,))
+            self._perm = inds
+        else:
+            self._perm = np.random.permutation(np.arange(len(self._roidb)))
+        self._cur = 0
+
+    def _get_next_minibatch_inds(self):
+        """Return the roidb indices for the next minibatch."""
+        if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
+            self._shuffle_roidb_inds()
+
+        db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
+        self._cur += cfg.TRAIN.IMS_PER_BATCH
+        return db_inds
+
+    def _get_next_minibatch(self):
+        """Return the blobs to be used for the next minibatch.
+
+        If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
+        separate process and made available through self._blob_queue.
+        """
+        if cfg.TRAIN.USE_PREFETCH:
+            return self._blob_queue.get()
+        else:
+            db_inds = self._get_next_minibatch_inds()
+            minibatch_db = [self._roidb[i] for i in db_inds]
+            return get_minibatch(minibatch_db, self._num_classes)
+
+    def set_roidb(self, roidb):
+        """Set the roidb to be used by this layer during training."""
+        self._roidb = roidb
+        self._shuffle_roidb_inds()
+        if cfg.TRAIN.USE_PREFETCH:
+            self._blob_queue = Queue(10)
+            self._prefetch_process = BlobFetcher(self._blob_queue,
+                                                 self._roidb,
+                                                 self._num_classes)
+            self._prefetch_process.start()
+            # Terminate the child process when the parent exists
+            def cleanup():
+                print 'Terminating BlobFetcher'
+                self._prefetch_process.terminate()
+                self._prefetch_process.join()
+            import atexit
+            atexit.register(cleanup)
+
+    def setup(self, bottom, top):
+        """Setup the RoIDataLayer."""
+
+        # parse the layer parameter string, which must be valid YAML
+        layer_params = yaml.load(self.param_str)
+
+        self._num_classes = layer_params['num_classes']
+
+        self._name_to_top_map = {}
+
+        # data blob: holds a batch of N images, each with 3 channels
+        idx = 0
+        top[idx].reshape(cfg.TRAIN.IMS_PER_BATCH, 3,
+            max(cfg.TRAIN.SCALES), cfg.TRAIN.MAX_SIZE)
+        self._name_to_top_map['data'] = idx
+        idx += 1
+
+        if cfg.TRAIN.HAS_RPN:
+            top[idx].reshape(1, 3)
+            self._name_to_top_map['im_info'] = idx
+            idx += 1
+
+            top[idx].reshape(1, 4)
+            self._name_to_top_map['gt_boxes'] = idx
+            idx += 1
+        else: # not using RPN
+            # rois blob: holds R regions of interest, each is a 5-tuple
+            # (n, x1, y1, x2, y2) specifying an image batch index n and a
+            # rectangle (x1, y1, x2, y2)
+            top[idx].reshape(1, 5, 1, 1)
+            self._name_to_top_map['rois'] = idx
+            idx += 1
+
+            # labels blob: R categorical labels in [0, ..., K] for K foreground
+            # classes plus background
+            top[idx].reshape(1, 1, 1, 1)
+            self._name_to_top_map['labels'] = idx
+            idx += 1
+
+            if cfg.TRAIN.BBOX_REG:
+                # bbox_targets blob: R bounding-box regression targets with 4
+                # targets per class
+                num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else self._num_classes
+                top[idx].reshape(1, num_reg_class * 4, 1, 1)
+                self._name_to_top_map['bbox_targets'] = idx
+                idx += 1
+
+                # bbox_inside_weights blob: At most 4 targets per roi are active;
+                # thisbinary vector sepcifies the subset of active targets
+                top[idx].reshape(1, num_reg_class * 4, 1, 1)
+                self._name_to_top_map['bbox_inside_weights'] = idx
+                idx += 1
+
+                top[idx].reshape(1, num_reg_class * 4, 1, 1)
+                self._name_to_top_map['bbox_outside_weights'] = idx
+                idx += 1
+
+        print 'RoiDataLayer: name_to_top:', self._name_to_top_map
+        assert len(top) == len(self._name_to_top_map)
+
+    def forward(self, bottom, top):
+        """Get blobs and copy them into this layer's top blob vector."""
+        blobs = self._get_next_minibatch()
+
+        for blob_name, blob in blobs.iteritems():
+            top_ind = self._name_to_top_map[blob_name]
+            shape = blob.shape
+            if len(shape) == 1:
+                blob = blob.reshape(blob.shape[0], 1, 1, 1)
+            if len(shape) == 2 and blob_name != 'im_info':
+                blob = blob.reshape(blob.shape[0], blob.shape[1], 1, 1)
+            top[top_ind].reshape(*(blob.shape))
+            # Copy data into net's input blobs
+            top[top_ind].data[...] = blob.astype(np.float32, copy=False)
+
+    def backward(self, top, propagate_down, bottom):
+        """This layer does not propagate gradients."""
+        pass
+
+    def reshape(self, bottom, top):
+        """Reshaping happens during the call to forward."""
+        pass
+
+class BlobFetcher(Process):
+    """Experimental class for prefetching blobs in a separate process."""
+    def __init__(self, queue, roidb, num_classes):
+        super(BlobFetcher, self).__init__()
+        self._queue = queue
+        self._roidb = roidb
+        self._num_classes = num_classes
+        self._perm = None
+        self._cur = 0
+        self._shuffle_roidb_inds()
+        # fix the random seed for reproducibility
+        np.random.seed(cfg.RNG_SEED)
+
+    def _shuffle_roidb_inds(self):
+        """Randomly permute the training roidb."""
+        # TODO(rbg): remove duplicated code
+        self._perm = np.random.permutation(np.arange(len(self._roidb)))
+        self._cur = 0
+
+    def _get_next_minibatch_inds(self):
+        """Return the roidb indices for the next minibatch."""
+        # TODO(rbg): remove duplicated code
+        if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):
+            self._shuffle_roidb_inds()
+
+        db_inds = self._perm[self._cur:self._cur + cfg.TRAIN.IMS_PER_BATCH]
+        self._cur += cfg.TRAIN.IMS_PER_BATCH
+        return db_inds
+
+    def run(self):
+        print 'BlobFetcher started'
+        while True:
+            db_inds = self._get_next_minibatch_inds()
+            minibatch_db = [self._roidb[i] for i in db_inds]
+            blobs = get_minibatch(minibatch_db, self._num_classes)
+            self._queue.put(blobs)
diff --git a/examples/rfcn/lib/roi_data_layer/minibatch.py b/examples/rfcn/lib/roi_data_layer/minibatch.py
new file mode 100644
index 000000000..a823547ed
--- /dev/null
+++ b/examples/rfcn/lib/roi_data_layer/minibatch.py
@@ -0,0 +1,219 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Compute minibatch blobs for training a Fast R-CNN network."""
+
+import numpy as np
+import numpy.random as npr
+import cv2
+import cPickle
+import gzip
+from fast_rcnn.config import cfg
+from utils.blob import prep_im_for_blob, im_list_to_blob
+
+def get_minibatch(roidb, num_classes):
+    """Given a roidb, construct a minibatch sampled from it."""
+    num_images = len(roidb)
+    num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else num_classes
+    # Sample random scales to use for each image in this batch
+    random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),
+                                    size=num_images)
+    assert(cfg.TRAIN.BATCH_SIZE % num_images == 0) or (cfg.TRAIN.BATCH_SIZE == -1), \
+        'num_images ({}) must divide BATCH_SIZE ({})'. \
+        format(num_images, cfg.TRAIN.BATCH_SIZE)
+    rois_per_image = np.inf if cfg.TRAIN.BATCH_SIZE == -1 else cfg.TRAIN.BATCH_SIZE / num_images
+    fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
+
+    # Get the input image blob, formatted for caffe
+    im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
+
+    blobs = {'data': im_blob}
+
+    if cfg.TRAIN.HAS_RPN:
+        assert len(im_scales) == 1, "Single batch only"
+        assert len(roidb) == 1, "Single batch only"
+        # gt boxes: (x1, y1, x2, y2, cls)
+        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
+        gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
+        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :] * im_scales[0]
+        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
+        blobs['gt_boxes'] = gt_boxes
+        blobs['im_info'] = np.array(
+            [[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],
+            dtype=np.float32)
+    else: # not using RPN
+        # Now, build the region of interest and label blobs
+        rois_blob = np.zeros((0, 5), dtype=np.float32)
+        labels_blob = np.zeros((0), dtype=np.float32)
+        bbox_targets_blob = np.zeros((0, 4 * num_reg_class), dtype=np.float32)
+        bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)
+        # all_overlaps = []
+        for im_i in xrange(num_images):
+            labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \
+                = _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image,
+                               num_classes)
+
+            # Add to RoIs blob
+            rois = _project_im_rois(im_rois, im_scales[im_i])
+            batch_ind = im_i * np.ones((rois.shape[0], 1))
+            rois_blob_this_image = np.hstack((batch_ind, rois))
+            rois_blob = np.vstack((rois_blob, rois_blob_this_image))
+
+            # Add to labels, bbox targets, and bbox loss blobs
+            labels_blob = np.hstack((labels_blob, labels))
+            bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))
+            bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))
+            # all_overlaps = np.hstack((all_overlaps, overlaps))
+
+        # For debug visualizations
+        # _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps)
+
+        blobs['rois'] = rois_blob
+        blobs['labels'] = labels_blob
+
+        if cfg.TRAIN.BBOX_REG:
+            blobs['bbox_targets'] = bbox_targets_blob
+            blobs['bbox_inside_weights'] = bbox_inside_blob
+            blobs['bbox_outside_weights'] = \
+                np.array(bbox_inside_blob > 0).astype(np.float32)
+
+    return blobs
+
+def _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):
+    """Generate a random sample of RoIs comprising foreground and background
+    examples.
+    """
+    # label = class RoI has max overlap with
+    labels = roidb['max_classes']
+    overlaps = roidb['max_overlaps']
+    rois = roidb['boxes']
+
+    # Select foreground RoIs as those with >= FG_THRESH overlap
+    fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
+    # Guard against the case when an image has fewer than fg_rois_per_image
+    # foreground RoIs
+    fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)
+    # Sample foreground regions without replacement
+    if fg_inds.size > 0:
+        fg_inds = npr.choice(
+                fg_inds, size=fg_rois_per_this_image, replace=False)
+
+    # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
+    bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
+                       (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
+    # Compute number of background RoIs to take from this image (guarding
+    # against there being fewer than desired)
+    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
+    bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
+                                        bg_inds.size)
+    # Sample foreground regions without replacement
+    if bg_inds.size > 0:
+        bg_inds = npr.choice(
+                bg_inds, size=bg_rois_per_this_image, replace=False)
+
+    # The indices that we're selecting (both fg and bg)
+    keep_inds = np.append(fg_inds, bg_inds)
+    # Select sampled values from various arrays:
+    labels = labels[keep_inds]
+    # Clamp labels for the background RoIs to 0
+    labels[fg_rois_per_this_image:] = 0
+    overlaps = overlaps[keep_inds]
+    rois = rois[keep_inds]
+
+    bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(
+            roidb['bbox_targets'][keep_inds, :], num_classes)
+
+    return labels, overlaps, rois, bbox_targets, bbox_inside_weights
+
+def _get_image_blob(roidb, scale_inds):
+    """Builds an input blob from the images in the roidb at the specified
+    scales.
+    """
+    num_images = len(roidb)
+    processed_ims = []
+    im_scales = []
+    for i in xrange(num_images):
+        im = None
+        if cfg.TRAIN.FORMAT == "pickle":
+            with gzip(roidb[i][image], 'rb') as f:
+                im = cPickle.load(f)
+        else:
+            im = cv2.imread(roidb[i]['image'])
+            # print roidb[i]['image']
+        if roidb[i]['flipped']:
+            im = im[:, ::-1, :]
+        target_size = cfg.TRAIN.SCALES[scale_inds[i]]
+        im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
+                                        cfg.TRAIN.MAX_SIZE)
+        im_scales.append(im_scale)
+        processed_ims.append(im)
+
+    # Create a blob to hold the input images
+    blob = im_list_to_blob(processed_ims)
+
+    return blob, im_scales
+
+def _project_im_rois(im_rois, im_scale_factor):
+    """Project image RoIs into the rescaled training image."""
+    rois = im_rois * im_scale_factor
+    return rois
+
+def _get_bbox_regression_labels(bbox_target_data, num_classes):
+    """Bounding-box regression targets are stored in a compact form in the
+    roidb.
+
+    This function expands those targets into the 4-of-4*K representation used
+    by the network (i.e. only one class has non-zero targets). The loss weights
+    are similarly expanded.
+
+    Returns:
+        bbox_target_data (ndarray): N x 4K blob of regression targets
+        bbox_inside_weights (ndarray): N x 4K blob of loss weights
+    """
+    clss = bbox_target_data[:, 0]
+    num_reg_class = 2 if cfg.TRAIN.AGNOSTIC else num_classes
+    bbox_targets = np.zeros((clss.size, 4 * num_reg_class), dtype=np.float32)
+    bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
+    inds = np.where(clss > 0)[0]
+
+    if cfg.TRAIN.AGNOSTIC:
+        for ind in inds:
+            cls = clss[ind]
+            start = 4 * (1 if cls > 0 else 0)
+            end = start + 4
+            bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
+            bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
+    else:
+        for ind in inds:
+            cls = clss[ind]
+            start = 4 * cls
+            end = start + 4
+            bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
+            bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
+
+    return bbox_targets, bbox_inside_weights
+
+def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):
+    """Visualize a mini-batch for debugging."""
+    import matplotlib.pyplot as plt
+    for i in xrange(rois_blob.shape[0]):
+        rois = rois_blob[i, :]
+        im_ind = rois[0]
+        roi = rois[1:]
+        im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()
+        im += cfg.PIXEL_MEANS
+        im = im[:, :, (2, 1, 0)]
+        im = im.astype(np.uint8)
+        cls = labels_blob[i]
+        plt.imshow(im)
+        print 'class: ', cls, ' overlap: ', overlaps[i]
+        plt.gca().add_patch(
+            plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],
+                          roi[3] - roi[1], fill=False,
+                          edgecolor='r', linewidth=3)
+            )
+        plt.show()
diff --git a/examples/rfcn/lib/roi_data_layer/roidb.py b/examples/rfcn/lib/roi_data_layer/roidb.py
new file mode 100644
index 000000000..05db5b513
--- /dev/null
+++ b/examples/rfcn/lib/roi_data_layer/roidb.py
@@ -0,0 +1,135 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Transform a roidb into a trainable roidb by adding a bunch of metadata."""
+
+import numpy as np
+from fast_rcnn.config import cfg
+from fast_rcnn.bbox_transform import bbox_transform
+from utils.cython_bbox import bbox_overlaps
+import PIL
+
+def prepare_roidb(imdb):
+    """Enrich the imdb's roidb by adding some derived quantities that
+    are useful for training. This function precomputes the maximum
+    overlap, taken over ground-truth boxes, between each ROI and
+    each ground-truth box. The class with maximum overlap is also
+    recorded.
+    """
+    sizes = [PIL.Image.open(imdb.image_path_at(i)).size
+             for i in xrange(imdb.num_images)]
+    roidb = imdb.roidb
+    for i in xrange(len(imdb.image_index)):
+        roidb[i]['image'] = imdb.image_path_at(i)
+        roidb[i]['width'] = sizes[i][0]
+        roidb[i]['height'] = sizes[i][1]
+        # need gt_overlaps as a dense array for argmax
+        gt_overlaps = roidb[i]['gt_overlaps'].toarray()
+        # max overlap with gt over classes (columns)
+        max_overlaps = gt_overlaps.max(axis=1)
+        # gt class that had the max overlap
+        max_classes = gt_overlaps.argmax(axis=1)
+        roidb[i]['max_classes'] = max_classes
+        roidb[i]['max_overlaps'] = max_overlaps
+        # sanity checks
+        # max overlap of 0 => class should be zero (background)
+        zero_inds = np.where(max_overlaps == 0)[0]
+        assert all(max_classes[zero_inds] == 0)
+        # max overlap > 0 => class should not be zero (must be a fg class)
+        nonzero_inds = np.where(max_overlaps > 0)[0]
+        assert all(max_classes[nonzero_inds] != 0)
+
+def add_bbox_regression_targets(roidb):
+    """Add information needed to train bounding-box regressors."""
+    assert len(roidb) > 0
+    assert 'max_classes' in roidb[0], 'Did you call prepare_roidb first?'
+
+    num_images = len(roidb)
+    # Infer number of classes from the number of columns in gt_overlaps
+    num_reg_classes = 2 if cfg.TRAIN.AGNOSTIC else roidb[0]['gt_overlaps'].shape[1]
+    for im_i in xrange(num_images):
+        rois = roidb[im_i]['boxes']
+        max_overlaps = roidb[im_i]['max_overlaps']
+        max_classes = roidb[im_i]['max_classes']
+        roidb[im_i]['bbox_targets'] = \
+            _compute_targets(rois, max_overlaps, max_classes)
+
+    if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
+        # Use fixed / precomputed "means" and "stds" instead of empirical values
+        means = np.tile(
+            np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (num_reg_classes, 1))
+        stds = np.tile(
+            np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (num_reg_classes, 1))
+    else:
+        # Compute values needed for means and stds
+        # var(x) = E(x^2) - E(x)^2
+        class_counts = np.zeros((num_reg_classes, 1)) + cfg.EPS
+        sums = np.zeros((num_reg_classes, 4))
+        squared_sums = np.zeros((num_reg_classes, 4))
+        for im_i in xrange(num_images):
+            targets = roidb[im_i]['bbox_targets']
+            for cls in xrange(1, num_reg_classes):
+                cls_inds = np.where(targets[:, 0] > 0)[0] if cfg.TRAIN.AGNOSTIC \
+                    else np.where(targets[:, 0] == cls)[0]
+                if cls_inds.size > 0:
+                    class_counts[cls] += cls_inds.size
+                    sums[cls, :] += targets[cls_inds, 1:].sum(axis=0)
+                    squared_sums[cls, :] += \
+                        (targets[cls_inds, 1:] ** 2).sum(axis=0)
+
+        means = sums / class_counts
+        stds = np.sqrt(squared_sums / class_counts - means ** 2)
+
+    print 'bbox target means:'
+    print means
+    print means[1:, :].mean(axis=0) # ignore bg class
+    print 'bbox target stdevs:'
+    print stds
+    print stds[1:, :].mean(axis=0) # ignore bg class
+
+    # Normalize targets
+    if cfg.TRAIN.BBOX_NORMALIZE_TARGETS:
+        print "Normalizing targets"
+        for im_i in xrange(num_images):
+            targets = roidb[im_i]['bbox_targets']
+            for cls in xrange(1, num_reg_classes):
+                cls_inds = np.where(targets[:, 0] > 0) if cfg.TRAIN.AGNOSTIC \
+                    else np.where(targets[:, 0] == cls)[0]
+                roidb[im_i]['bbox_targets'][cls_inds, 1:] -= means[cls, :]
+                roidb[im_i]['bbox_targets'][cls_inds, 1:] /= stds[cls, :]
+    else:
+        print "NOT normalizing targets"
+
+    # These values will be needed for making predictions
+    # (the predicts will need to be unnormalized and uncentered)
+    return means.ravel(), stds.ravel()
+
+def _compute_targets(rois, overlaps, labels):
+    """Compute bounding-box regression targets for an image."""
+    # Indices of ground-truth ROIs
+    gt_inds = np.where(overlaps == 1)[0]
+    if len(gt_inds) == 0:
+        # Bail if the image has no ground-truth ROIs
+        return np.zeros((rois.shape[0], 5), dtype=np.float32)
+    # Indices of examples for which we try to make predictions
+    ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
+
+    # Get IoU overlap between each ex ROI and gt ROI
+    ex_gt_overlaps = bbox_overlaps(
+        np.ascontiguousarray(rois[ex_inds, :], dtype=np.float),
+        np.ascontiguousarray(rois[gt_inds, :], dtype=np.float))
+
+    # Find which gt ROI each ex ROI has max overlap with:
+    # this will be the ex ROI's gt target
+    gt_assignment = ex_gt_overlaps.argmax(axis=1)
+    gt_rois = rois[gt_inds[gt_assignment], :]
+    ex_rois = rois[ex_inds, :]
+
+    targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
+    targets[ex_inds, 0] = labels[ex_inds]
+    targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
+    return targets
diff --git a/examples/rfcn/lib/rpn/README.md b/examples/rfcn/lib/rpn/README.md
new file mode 100644
index 000000000..80abf48e2
--- /dev/null
+++ b/examples/rfcn/lib/rpn/README.md
@@ -0,0 +1,23 @@
+### `rpn` module overview
+
+##### `generate_anchors.py`
+
+Generates a regular grid of multi-scale, multi-aspect anchor boxes.
+
+##### `proposal_layer.py`
+
+Converts RPN outputs (per-anchor scores and bbox regression estimates) into object proposals.
+
+##### `anchor_target_layer.py` 
+
+Generates training targets/labels for each anchor. Classification labels are 1 (object), 0 (not object) or -1 (ignore).
+Bbox regression targets are specified when the classification label is > 0.
+
+##### `proposal_target_layer.py`
+
+Generates training targets/labels for each object proposal: classification labels 0 - K (bg or object class 1, ... , K)
+and bbox regression targets in that case that the label is > 0.
+
+##### `generate.py`
+
+Generate object detection proposals from an imdb using an RPN.
diff --git a/examples/rfcn/lib/rpn/__init__.py b/examples/rfcn/lib/rpn/__init__.py
new file mode 100644
index 000000000..23b31b2da
--- /dev/null
+++ b/examples/rfcn/lib/rpn/__init__.py
@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
diff --git a/examples/rfcn/lib/rpn/anchor_target_layer.py b/examples/rfcn/lib/rpn/anchor_target_layer.py
new file mode 100644
index 000000000..48f24e6a3
--- /dev/null
+++ b/examples/rfcn/lib/rpn/anchor_target_layer.py
@@ -0,0 +1,288 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
+
+import os
+import caffe
+import yaml
+from fast_rcnn.config import cfg
+import numpy as np
+import numpy.random as npr
+from generate_anchors import generate_anchors
+from utils.cython_bbox import bbox_overlaps
+from fast_rcnn.bbox_transform import bbox_transform
+
+DEBUG = False
+
+class AnchorTargetLayer(caffe.Layer):
+    """
+    Assign anchors to ground-truth targets. Produces anchor classification
+    labels and bounding-box regression targets.
+    """
+
+    def setup(self, bottom, top):
+        layer_params = yaml.load(self.param_str)
+        anchor_scales = layer_params.get('scales', (8, 16, 32))
+        self._anchors = generate_anchors(scales=np.array(anchor_scales))
+        self._num_anchors = self._anchors.shape[0]
+        self._feat_stride = layer_params['feat_stride']
+
+        if DEBUG:
+            print 'anchors:'
+            print self._anchors
+            print 'anchor shapes:'
+            print np.hstack((
+                self._anchors[:, 2::4] - self._anchors[:, 0::4],
+                self._anchors[:, 3::4] - self._anchors[:, 1::4],
+            ))
+            self._counts = cfg.EPS
+            self._sums = np.zeros((1, 4))
+            self._squared_sums = np.zeros((1, 4))
+            self._fg_sum = 0
+            self._bg_sum = 0
+            self._count = 0
+
+        # allow boxes to sit over the edge by a small amount
+        self._allowed_border = layer_params.get('allowed_border', 0)
+
+        height, width = bottom[0].data.shape[-2:]
+        if DEBUG:
+            print 'AnchorTargetLayer: height', height, 'width', width
+
+        A = self._num_anchors
+        # labels
+        top[0].reshape(1, 1, A * height, width)
+        # bbox_targets
+        top[1].reshape(1, A * 4, height, width)
+        # bbox_inside_weights
+        top[2].reshape(1, A * 4, height, width)
+        # bbox_outside_weights
+        top[3].reshape(1, A * 4, height, width)
+
+    def forward(self, bottom, top):
+        # Algorithm:
+        #
+        # for each (H, W) location i
+        #   generate 9 anchor boxes centered on cell i
+        #   apply predicted bbox deltas at cell i to each of the 9 anchors
+        # filter out-of-image anchors
+        # measure GT overlap
+
+        assert bottom[0].data.shape[0] == 1, \
+            'Only single item batches are supported'
+
+        # map of shape (..., H, W)
+        height, width = bottom[0].data.shape[-2:]
+        # GT boxes (x1, y1, x2, y2, label)
+        gt_boxes = bottom[1].data
+        # im_info
+        im_info = bottom[2].data[0, :]
+
+        if DEBUG:
+            print ''
+            print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
+            print 'scale: {}'.format(im_info[2])
+            print 'height, width: ({}, {})'.format(height, width)
+            print 'rpn: gt_boxes.shape', gt_boxes.shape
+            print 'rpn: gt_boxes', gt_boxes
+
+        # 1. Generate proposals from bbox deltas and shifted anchors
+        shift_x = np.arange(0, width) * self._feat_stride
+        shift_y = np.arange(0, height) * self._feat_stride
+        shift_x, shift_y = np.meshgrid(shift_x, shift_y)
+        shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
+                            shift_x.ravel(), shift_y.ravel())).transpose()
+        # add A anchors (1, A, 4) to
+        # cell K shifts (K, 1, 4) to get
+        # shift anchors (K, A, 4)
+        # reshape to (K*A, 4) shifted anchors
+        A = self._num_anchors
+        K = shifts.shape[0]
+        all_anchors = (self._anchors.reshape((1, A, 4)) +
+                       shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
+        all_anchors = all_anchors.reshape((K * A, 4))
+        total_anchors = int(K * A)
+
+        # only keep anchors inside the image
+        inds_inside = np.where(
+            (all_anchors[:, 0] >= -self._allowed_border) &
+            (all_anchors[:, 1] >= -self._allowed_border) &
+            (all_anchors[:, 2] < im_info[1] + self._allowed_border) &  # width
+            (all_anchors[:, 3] < im_info[0] + self._allowed_border)    # height
+        )[0]
+
+        if DEBUG:
+            print 'total_anchors', total_anchors
+            print 'inds_inside', len(inds_inside)
+
+        # keep only inside anchors
+        anchors = all_anchors[inds_inside, :]
+        if DEBUG:
+            print 'anchors.shape', anchors.shape
+
+        # label: 1 is positive, 0 is negative, -1 is dont care
+        labels = np.empty((len(inds_inside), ), dtype=np.float32)
+        labels.fill(-1)
+
+        # overlaps between the anchors and the gt boxes
+        # overlaps (ex, gt)
+        gt_boxes = gt_boxes.reshape(gt_boxes.shape[0], gt_boxes.shape[1])
+        overlaps = bbox_overlaps(
+            np.ascontiguousarray(anchors, dtype=np.float),
+            np.ascontiguousarray(gt_boxes, dtype=np.float))
+        argmax_overlaps = overlaps.argmax(axis=1)
+        max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
+        gt_argmax_overlaps = overlaps.argmax(axis=0)
+        gt_max_overlaps = overlaps[gt_argmax_overlaps,
+                                   np.arange(overlaps.shape[1])]
+        gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
+
+        if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
+            # assign bg labels first so that positive labels can clobber them
+            labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
+
+        # fg label: for each gt, anchor with highest overlap
+        labels[gt_argmax_overlaps] = 1
+
+        # fg label: above threshold IOU
+        labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
+
+        if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
+            # assign bg labels last so that negative labels can clobber positives
+            labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
+
+        # subsample positive labels if we have too many
+        num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
+        fg_inds = np.where(labels == 1)[0]
+        if len(fg_inds) > num_fg:
+            disable_inds = npr.choice(
+                fg_inds, size=(len(fg_inds) - num_fg), replace=False)
+            labels[disable_inds] = -1
+
+        # subsample negative labels if we have too many
+        num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
+        bg_inds = np.where(labels == 0)[0]
+        if len(bg_inds) > num_bg:
+            disable_inds = npr.choice(
+                bg_inds, size=(len(bg_inds) - num_bg), replace=False)
+            labels[disable_inds] = -1
+            #print "was %s inds, disabling %s, now %s inds" % (
+                #len(bg_inds), len(disable_inds), np.sum(labels == 0))
+
+        bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
+        bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
+
+        bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
+        bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
+
+        bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
+        if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
+            # uniform weighting of examples (given non-uniform sampling)
+            num_examples = np.sum(labels >= 0)
+            positive_weights = np.ones((1, 4)) * 1.0 / num_examples
+            negative_weights = np.ones((1, 4)) * 1.0 / num_examples
+        else:
+            assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
+                    (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
+            positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
+                                np.sum(labels == 1))
+            negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
+                                np.sum(labels == 0))
+        bbox_outside_weights[labels == 1, :] = positive_weights
+        bbox_outside_weights[labels == 0, :] = negative_weights
+
+        if DEBUG:
+            self._sums += bbox_targets[labels == 1, :].sum(axis=0)
+            self._squared_sums += (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
+            self._counts += np.sum(labels == 1)
+            means = self._sums / self._counts
+            stds = np.sqrt(self._squared_sums / self._counts - means ** 2)
+            print 'means:'
+            print means
+            print 'stdevs:'
+            print stds
+
+        # map up to original set of anchors
+        labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
+        bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
+        bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)
+        bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)
+
+        if DEBUG:
+            print 'rpn: max max_overlap', np.max(max_overlaps)
+            print 'rpn: num_positive', np.sum(labels == 1)
+            print 'rpn: num_negative', np.sum(labels == 0)
+            self._fg_sum += np.sum(labels == 1)
+            self._bg_sum += np.sum(labels == 0)
+            self._count += 1
+            print 'rpn: num_positive avg', self._fg_sum / self._count
+            print 'rpn: num_negative avg', self._bg_sum / self._count
+
+        # labels
+        labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
+        labels = labels.reshape((1, 1, A * height, width))
+        top[0].reshape(*labels.shape)
+        top[0].data[...] = labels
+
+        # bbox_targets
+        bbox_targets = bbox_targets \
+            .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
+        top[1].reshape(*bbox_targets.shape)
+        top[1].data[...] = bbox_targets
+
+        # bbox_inside_weights
+        bbox_inside_weights = bbox_inside_weights \
+            .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
+        assert bbox_inside_weights.shape[2] == height
+        assert bbox_inside_weights.shape[3] == width
+        top[2].reshape(*bbox_inside_weights.shape)
+        top[2].data[...] = bbox_inside_weights
+
+        # bbox_outside_weights
+        bbox_outside_weights = bbox_outside_weights \
+            .reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
+        assert bbox_outside_weights.shape[2] == height
+        assert bbox_outside_weights.shape[3] == width
+        top[3].reshape(*bbox_outside_weights.shape)
+        top[3].data[...] = bbox_outside_weights
+
+    def backward(self, top, propagate_down, bottom):
+        """This layer does not propagate gradients."""
+        pass
+
+    def reshape(self, bottom, top):
+        """Reshaping happens during the call to forward."""
+        pass
+
+
+def _unmap(data, count, inds, fill=0):
+    """ Unmap a subset of item (data) back to the original set of items (of
+    size count) """
+    if len(data.shape) == 1:
+        ret = np.empty((count, ), dtype=np.float32)
+        ret.fill(fill)
+        ret[inds] = data
+    else:
+        ret = np.empty((count, ) + data.shape[1:], dtype=np.float32)
+        ret.fill(fill)
+        ret[inds, :] = data
+    return ret
+
+
+def _compute_targets(ex_rois, gt_rois):
+    """Compute bounding-box regression targets for an image."""
+
+    assert ex_rois.shape[0] == gt_rois.shape[0]
+    assert ex_rois.shape[1] == 4
+    assert gt_rois.shape[1] == 5
+
+    targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
+    if cfg.TRAIN.RPN_NORMALIZE_TARGETS:
+        assert cfg.TRAIN.RPN_NORMALIZE_MEANS is not None
+        assert cfg.TRAIN.RPN_NORMALIZE_STDS is not None
+        targets -= cfg.TRAIN.RPN_NORMALIZE_MEANS
+        targets /= cfg.TRAIN.RPN_NORMALIZE_STDS
+    return targets
diff --git a/examples/rfcn/lib/rpn/generate.py b/examples/rfcn/lib/rpn/generate.py
new file mode 100644
index 000000000..960d1985e
--- /dev/null
+++ b/examples/rfcn/lib/rpn/generate.py
@@ -0,0 +1,225 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+from fast_rcnn.config import cfg
+from fast_rcnn.train import filter_roidb
+from utils.blob import im_list_to_blob
+from utils.timer import Timer
+from generate_anchors import generate_anchors
+from utils.cython_bbox import bbox_overlaps
+from fast_rcnn.bbox_transform import bbox_transform
+import numpy as np
+import cv2
+
+def _vis_proposals(im, dets, thresh=0.5):
+    """Draw detected bounding boxes."""
+    inds = np.where(dets[:, -1] >= thresh)[0]
+    if len(inds) == 0:
+        return
+
+    class_name = 'obj'
+    im = im[:, :, (2, 1, 0)]
+    fig, ax = plt.subplots(figsize=(12, 12))
+    ax.imshow(im, aspect='equal')
+    for i in inds:
+        bbox = dets[i, :4]
+        score = dets[i, -1]
+
+        ax.add_patch(
+            plt.Rectangle((bbox[0], bbox[1]),
+                          bbox[2] - bbox[0],
+                          bbox[3] - bbox[1], fill=False,
+                          edgecolor='red', linewidth=3.5)
+            )
+        ax.text(bbox[0], bbox[1] - 2,
+                '{:s} {:.3f}'.format(class_name, score),
+                bbox=dict(facecolor='blue', alpha=0.5),
+                fontsize=14, color='white')
+
+    ax.set_title(('{} detections with '
+                  'p({} | box) >= {:.1f}').format(class_name, class_name,
+                                                  thresh),
+                  fontsize=14)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.draw()
+
+def _get_image_blob(im):
+    """Converts an image into a network input.
+
+    Arguments:
+        im (ndarray): a color image in BGR order
+
+    Returns:
+        blob (ndarray): a data blob holding an image pyramid
+        im_scale_factors (list): list of image scales (relative to im) used
+            in the image pyramid
+    """
+    im_orig = im.astype(np.float32, copy=True)
+    im_orig -= cfg.PIXEL_MEANS
+
+    im_shape = im_orig.shape
+    im_size_min = np.min(im_shape[0:2])
+    im_size_max = np.max(im_shape[0:2])
+
+    processed_ims = []
+
+    assert len(cfg.TEST.SCALES) == 1
+    target_size = cfg.TEST.SCALES[0]
+
+    im_scale = float(target_size) / float(im_size_min)
+    # Prevent the biggest axis from being more than MAX_SIZE
+    if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
+        im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
+    im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
+                    interpolation=cv2.INTER_LINEAR)
+    im_info = np.hstack((im.shape[:2], im_scale))[np.newaxis, :]
+    processed_ims.append(im)
+
+    # Create a blob to hold the input images
+    blob = im_list_to_blob(processed_ims)
+
+    return blob, im_info
+
+def im_proposals(net, im):
+    """Generate RPN proposals on a single image."""
+    blobs = {}
+    blobs['data'], blobs['im_info'] = _get_image_blob(im)
+    net.blobs['data'].reshape(*(blobs['data'].shape))
+    net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
+    blobs_out = net.forward(
+            data=blobs['data'].astype(np.float32, copy=False),
+            im_info=blobs['im_info'].astype(np.float32, copy=False))
+
+    scale = blobs['im_info'][0, 2]
+    boxes = blobs_out['rois'][:, 1:].copy() / scale
+    scores = blobs_out['scores'].copy()
+    return boxes, scores
+
+def imdb_proposals(net, imdb):
+    """Generate RPN proposals on all images in an imdb."""
+
+    _t = Timer()
+    imdb_boxes = [[] for _ in xrange(imdb.num_images)]
+    for i in xrange(imdb.num_images):
+        im = None
+        if cfg.TRAIN.FORMAT == 'pickle':
+            with open(imdb.image_path_at(i), 'rb') as f:
+                im = cPickle.load(f)
+        else:
+            im = cv2.imread(imdb.image_path_at(i))
+
+        _t.tic()
+        imdb_boxes[i], scores = im_proposals(net, im)
+        _t.toc()
+        print 'im_proposals: {:d}/{:d} {:.3f}s' \
+              .format(i + 1, imdb.num_images, _t.average_time)
+        if 0:
+            dets = np.hstack((imdb_boxes[i], scores))
+            # from IPython import embed; embed()
+            _vis_proposals(im, dets[:3, :], thresh=0.9)
+            plt.show()
+
+    return imdb_boxes
+
+
+def imdb_rpn_compute_stats(net, imdb, anchor_scales=(8,16,32),
+                           feature_stride=16):
+    raw_anchors = generate_anchors(scales=np.array(anchor_scales))
+    print raw_anchors.shape
+    sums = 0
+    squred_sums = 0
+    counts = 0
+    roidb = filter_roidb(imdb.roidb)
+    # Compute a map of input image size and output feature map blob
+    map_w = {}
+    map_h = {}
+    for i in xrange(50, cfg.TRAIN.MAX_SIZE + 10):
+        blobs = {
+            'data': np.zeros((1, 3, i, i)),
+            'im_info': np.asarray([[i, i, 1.0]])
+        }
+        net.blobs['data'].reshape(*(blobs['data'].shape))
+        net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
+        blobs_out = net.forward(
+            data=blobs['data'].astype(np.float32, copy=False),
+            im_info=blobs['im_info'].astype(np.float32, copy=False))
+        height, width = net.blobs['rpn/output'].data.shape[-2:]
+        map_w[i] = width
+        map_h[i] = height
+
+    for i in xrange(len(roidb)):
+        if not i % 5000:
+            print 'computing %d/%d' % (i, imdb.num_images)
+        im = None
+        if cfg.TRAIN.FORMAT == 'pickle':
+            with open(roidb[i]['image'], 'rb') as f:
+                im = cPickle.load(f)
+        else:
+            im = cv2.imread(roidb[i]['image'])
+
+        im_data, im_info = _get_image_blob(im)
+        gt_boxes = roidb[i]['boxes']
+        gt_boxes = gt_boxes * im_info[0, 2]
+        height = map_h[im_data.shape[2]]
+        width = map_w[im_data.shape[3]]
+        # 1. Generate proposals from bbox deltas and shifted anchors
+        shift_x = np.arange(0, width) * feature_stride
+        shift_y = np.arange(0, height) * feature_stride
+        shift_x, shift_y = np.meshgrid(shift_x, shift_y)
+        shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
+                            shift_x.ravel(), shift_y.ravel())).transpose()
+        # add A anchors (1, A, 4) to
+        # cell K shifts (K, 1, 4) to get
+        # shift anchors (K, A, 4)
+        # reshape to (K*A, 4) shifted anchors
+        A = raw_anchors.shape[0]
+        K = shifts.shape[0]
+        all_anchors = (raw_anchors.reshape((1, A, 4)) +
+                       shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
+        all_anchors = all_anchors.reshape((K * A, 4))
+
+        # only keep anchors inside the image
+        inds_inside = np.where(
+            (all_anchors[:, 0] >= 0) &
+            (all_anchors[:, 1] >= 0) &
+            (all_anchors[:, 2] < im_info[0, 1]) &  # width
+            (all_anchors[:, 3] < im_info[0, 0])  # height
+        )[0]
+
+        # keep only inside anchors
+        anchors = all_anchors[inds_inside, :]
+
+        overlaps = bbox_overlaps(
+            np.ascontiguousarray(anchors, dtype=np.float),
+            np.ascontiguousarray(gt_boxes, dtype=np.float))
+
+        # There are 2 types of bbox targets
+        # 1. anchor whose overlaps with gt is greater than RPN_POSITIVE_OVERLAP
+        argmax_overlaps = overlaps.argmax(axis=1)
+        max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
+        fg_inds = np.where(max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP)[0]
+        # 2. anchors which best match certain gt
+        gt_argmax_overlaps = overlaps.argmax(axis=0)
+        gt_max_overlaps = overlaps[gt_argmax_overlaps,
+                                   np.arange(overlaps.shape[1])]
+        gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
+        fg_inds = np.unique(np.hstack((fg_inds, gt_argmax_overlaps)))
+        gt_rois = gt_boxes[argmax_overlaps, :]
+
+        anchors = anchors[fg_inds, :]
+        gt_rois = gt_rois[fg_inds, :]
+        targets = bbox_transform(anchors, gt_rois[:, :4]).astype(np.float32, copy=False)
+        sums += targets.sum(axis=0)
+        squred_sums += (targets ** 2).sum(axis=0)
+        counts += targets.shape[0]
+
+    means = sums / counts
+    stds = np.sqrt(squred_sums / counts - means ** 2)
+    print means
+    print stds
+    return means, stds
diff --git a/examples/rfcn/lib/rpn/generate_anchors.py b/examples/rfcn/lib/rpn/generate_anchors.py
new file mode 100644
index 000000000..1125a801f
--- /dev/null
+++ b/examples/rfcn/lib/rpn/generate_anchors.py
@@ -0,0 +1,105 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
+
+import numpy as np
+
+# Verify that we compute the same anchors as Shaoqing's matlab implementation:
+#
+#    >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
+#    >> anchors
+#
+#    anchors =
+#
+#       -83   -39   100    56
+#      -175   -87   192   104
+#      -359  -183   376   200
+#       -55   -55    72    72
+#      -119  -119   136   136
+#      -247  -247   264   264
+#       -35   -79    52    96
+#       -79  -167    96   184
+#      -167  -343   184   360
+
+#array([[ -83.,  -39.,  100.,   56.],
+#       [-175.,  -87.,  192.,  104.],
+#       [-359., -183.,  376.,  200.],
+#       [ -55.,  -55.,   72.,   72.],
+#       [-119., -119.,  136.,  136.],
+#       [-247., -247.,  264.,  264.],
+#       [ -35.,  -79.,   52.,   96.],
+#       [ -79., -167.,   96.,  184.],
+#       [-167., -343.,  184.,  360.]])
+
+def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
+                     scales=2**np.arange(3, 6)):
+    """
+    Generate anchor (reference) windows by enumerating aspect ratios X
+    scales wrt a reference (0, 0, 15, 15) window.
+    """
+
+    base_anchor = np.array([1, 1, base_size, base_size]) - 1
+    ratio_anchors = _ratio_enum(base_anchor, ratios)
+    anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
+                         for i in xrange(ratio_anchors.shape[0])])
+    return anchors
+
+def _whctrs(anchor):
+    """
+    Return width, height, x center, and y center for an anchor (window).
+    """
+
+    w = anchor[2] - anchor[0] + 1
+    h = anchor[3] - anchor[1] + 1
+    x_ctr = anchor[0] + 0.5 * (w - 1)
+    y_ctr = anchor[1] + 0.5 * (h - 1)
+    return w, h, x_ctr, y_ctr
+
+def _mkanchors(ws, hs, x_ctr, y_ctr):
+    """
+    Given a vector of widths (ws) and heights (hs) around a center
+    (x_ctr, y_ctr), output a set of anchors (windows).
+    """
+
+    ws = ws[:, np.newaxis]
+    hs = hs[:, np.newaxis]
+    anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
+                         y_ctr - 0.5 * (hs - 1),
+                         x_ctr + 0.5 * (ws - 1),
+                         y_ctr + 0.5 * (hs - 1)))
+    return anchors
+
+def _ratio_enum(anchor, ratios):
+    """
+    Enumerate a set of anchors for each aspect ratio wrt an anchor.
+    """
+
+    w, h, x_ctr, y_ctr = _whctrs(anchor)
+    size = w * h
+    size_ratios = size / ratios
+    ws = np.round(np.sqrt(size_ratios))
+    hs = np.round(ws * ratios)
+    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
+    return anchors
+
+def _scale_enum(anchor, scales):
+    """
+    Enumerate a set of anchors for each scale wrt an anchor.
+    """
+
+    w, h, x_ctr, y_ctr = _whctrs(anchor)
+    ws = w * scales
+    hs = h * scales
+    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
+    return anchors
+
+if __name__ == '__main__':
+    import time
+    t = time.time()
+    a = generate_anchors()
+    print time.time() - t
+    print a
+    from IPython import embed; embed()
diff --git a/examples/rfcn/lib/rpn/proposal_layer.py b/examples/rfcn/lib/rpn/proposal_layer.py
new file mode 100644
index 000000000..cb85579d5
--- /dev/null
+++ b/examples/rfcn/lib/rpn/proposal_layer.py
@@ -0,0 +1,180 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
+
+import caffe
+import numpy as np
+import yaml
+from fast_rcnn.config import cfg
+from generate_anchors import generate_anchors
+from fast_rcnn.bbox_transform import bbox_transform_inv, clip_boxes
+from fast_rcnn.nms_wrapper import nms
+
+DEBUG = False
+
+class ProposalLayer(caffe.Layer):
+    """
+    Outputs object detection proposals by applying estimated bounding-box
+    transformations to a set of regular boxes (called "anchors").
+    """
+
+    def setup(self, bottom, top):
+        # parse the layer parameter string, which must be valid YAML
+        layer_params = yaml.load(self.param_str)
+
+        self._feat_stride = layer_params['feat_stride']
+        anchor_scales = layer_params.get('scales', (8, 16, 32))
+        self._anchors = generate_anchors(scales=np.array(anchor_scales))
+        self._num_anchors = self._anchors.shape[0]
+
+        if DEBUG:
+            print 'feat_stride: {}'.format(self._feat_stride)
+            print 'anchors:'
+            print self._anchors
+
+        # rois blob: holds R regions of interest, each is a 5-tuple
+        # (n, x1, y1, x2, y2) specifying an image batch index n and a
+        # rectangle (x1, y1, x2, y2)
+        top[0].reshape(1, 5)
+
+        # scores blob: holds scores for R regions of interest
+        if len(top) > 1:
+            top[1].reshape(1, 1, 1, 1)
+
+    def forward(self, bottom, top):
+        # Algorithm:
+        #
+        # for each (H, W) location i
+        #   generate A anchor boxes centered on cell i
+        #   apply predicted bbox deltas at cell i to each of the A anchors
+        # clip predicted boxes to image
+        # remove predicted boxes with either height or width < threshold
+        # sort all (proposal, score) pairs by score from highest to lowest
+        # take top pre_nms_topN proposals before NMS
+        # apply NMS with threshold 0.7 to remaining proposals
+        # take after_nms_topN proposals after NMS
+        # return the top proposals (-> RoIs top, scores top)
+
+        assert bottom[0].data.shape[0] == 1, \
+            'Only single item batches are supported'
+
+        #cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
+        cfg_key = str('TRAIN' if self.phase == 0 else 'TEST')
+        pre_nms_topN  = cfg[cfg_key].RPN_PRE_NMS_TOP_N
+        post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
+        nms_thresh    = cfg[cfg_key].RPN_NMS_THRESH
+        min_size      = cfg[cfg_key].RPN_MIN_SIZE
+
+        # the first set of _num_anchors channels are bg probs
+        # the second set are the fg probs, which we want
+        scores = bottom[0].data[:, self._num_anchors:, :, :]
+        bbox_deltas = bottom[1].data
+        im_info = bottom[2].data[0, :]
+
+        if DEBUG:
+            print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
+            print 'scale: {}'.format(im_info[2])
+
+        # 1. Generate proposals from bbox deltas and shifted anchors
+        height, width = scores.shape[-2:]
+
+        if DEBUG:
+            print 'score map size: {}'.format(scores.shape)
+
+        # Enumerate all shifts
+        shift_x = np.arange(0, width) * self._feat_stride
+        shift_y = np.arange(0, height) * self._feat_stride
+        shift_x, shift_y = np.meshgrid(shift_x, shift_y)
+        shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
+                            shift_x.ravel(), shift_y.ravel())).transpose()
+
+        # Enumerate all shifted anchors:
+        #
+        # add A anchors (1, A, 4) to
+        # cell K shifts (K, 1, 4) to get
+        # shift anchors (K, A, 4)
+        # reshape to (K*A, 4) shifted anchors
+        A = self._num_anchors
+        K = shifts.shape[0]
+        anchors = self._anchors.reshape((1, A, 4)) + \
+                  shifts.reshape((1, K, 4)).transpose((1, 0, 2))
+        anchors = anchors.reshape((K * A, 4))
+
+        # Transpose and reshape predicted bbox transformations to get them
+        # into the same order as the anchors:
+        #
+        # bbox deltas will be (1, 4 * A, H, W) format
+        # transpose to (1, H, W, 4 * A)
+        # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
+        # in slowest to fastest order
+        bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
+        if cfg_key == 'TRAIN' and cfg.TRAIN.RPN_NORMALIZE_TARGETS:
+            bbox_deltas *= cfg.TRAIN.RPN_NORMALIZE_STDS
+            bbox_deltas += cfg.TRAIN.RPN_NORMALIZE_MEANS
+
+        # Same story for the scores:
+        #
+        # scores are (1, A, H, W) format
+        # transpose to (1, H, W, A)
+        # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
+        scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
+
+        # Convert anchors into proposals via bbox transformations
+        proposals = bbox_transform_inv(anchors, bbox_deltas)
+
+        # 2. clip predicted boxes to image
+        proposals = clip_boxes(proposals, im_info[:2])
+
+        # 3. remove predicted boxes with either height or width < threshold
+        # (NOTE: convert min_size to input image scale stored in im_info[2])
+        keep = _filter_boxes(proposals, min_size * im_info[2])
+        proposals = proposals[keep, :]
+        scores = scores[keep]
+
+        # 4. sort all (proposal, score) pairs by score from highest to lowest
+        # 5. take top pre_nms_topN (e.g. 6000)
+        order = scores.ravel().argsort()[::-1]
+        if pre_nms_topN > 0:
+            order = order[:pre_nms_topN]
+        proposals = proposals[order, :]
+        scores = scores[order]
+
+        # 6. apply nms (e.g. threshold = 0.7)
+        # 7. take after_nms_topN (e.g. 300)
+        # 8. return the top proposals (-> RoIs top)
+        keep = nms(np.hstack((proposals, scores)), nms_thresh)
+        if post_nms_topN > 0:
+            keep = keep[:post_nms_topN]
+        proposals = proposals[keep, :]
+        scores = scores[keep]
+
+        # Output rois blob
+        # Our RPN implementation only supports a single input image, so all
+        # batch inds are 0
+        batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
+        blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
+        top[0].reshape(*(blob.shape))
+        top[0].data[...] = blob
+
+        # [Optional] output scores blob
+        if len(top) > 1:
+            top[1].reshape(*(scores.shape))
+            top[1].data[...] = scores
+
+    def backward(self, top, propagate_down, bottom):
+        """This layer does not propagate gradients."""
+        pass
+
+    def reshape(self, bottom, top):
+        """Reshaping happens during the call to forward."""
+        pass
+
+def _filter_boxes(boxes, min_size):
+    """Remove all boxes with any side smaller than min_size."""
+    ws = boxes[:, 2] - boxes[:, 0] + 1
+    hs = boxes[:, 3] - boxes[:, 1] + 1
+    keep = np.where((ws >= min_size) & (hs >= min_size))[0]
+    return keep
diff --git a/examples/rfcn/lib/rpn/proposal_target_layer.py b/examples/rfcn/lib/rpn/proposal_target_layer.py
new file mode 100644
index 000000000..c0e586a82
--- /dev/null
+++ b/examples/rfcn/lib/rpn/proposal_target_layer.py
@@ -0,0 +1,205 @@
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick and Sean Bell
+# --------------------------------------------------------
+
+import caffe
+import yaml
+import numpy as np
+import numpy.random as npr
+from fast_rcnn.config import cfg
+from fast_rcnn.bbox_transform import bbox_transform
+from utils.cython_bbox import bbox_overlaps
+
+DEBUG = False
+
+class ProposalTargetLayer(caffe.Layer):
+    """
+    Assign object detection proposals to ground-truth targets. Produces proposal
+    classification labels and bounding-box regression targets.
+    """
+
+    def setup(self, bottom, top):
+        layer_params = yaml.load(self.param_str)
+        self._num_classes = layer_params['num_classes']
+
+        # sampled rois (0, x1, y1, x2, y2)
+        top[0].reshape(1, 5, 1, 1)
+        # labels
+        top[1].reshape(1, 1, 1, 1)
+        # bbox_targets
+        top[2].reshape(1, self._num_classes * 4, 1, 1)
+        # bbox_inside_weights
+        top[3].reshape(1, self._num_classes * 4, 1, 1)
+        # bbox_outside_weights
+        top[4].reshape(1, self._num_classes * 4, 1, 1)
+
+    def forward(self, bottom, top):
+        # Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
+        # (i.e., rpn.proposal_layer.ProposalLayer), or any other source
+        all_rois = bottom[0].data
+        # GT boxes (x1, y1, x2, y2, label)
+        # TODO(rbg): it's annoying that sometimes I have extra info before
+        # and other times after box coordinates -- normalize to one format
+        gt_boxes = bottom[1].data
+        gt_boxes = gt_boxes.reshape(gt_boxes.shape[0], gt_boxes.shape[1])
+        # Include ground-truth boxes in the set of candidate rois
+        zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
+        all_rois = np.vstack(
+            (all_rois, np.hstack((zeros, gt_boxes[:, :-1])))
+        )
+
+        # Sanity check: single batch only
+        assert np.all(all_rois[:, 0] == 0), \
+                'Only single item batches are supported'
+
+        rois_per_image = np.inf if cfg.TRAIN.BATCH_SIZE == -1 else cfg.TRAIN.BATCH_SIZE
+        fg_rois_per_image = int(np.round(cfg.TRAIN.FG_FRACTION * rois_per_image))
+
+        # Sample rois with classification labels and bounding box regression
+        # targets
+        labels, rois, bbox_targets, bbox_inside_weights = _sample_rois(
+            all_rois, gt_boxes, fg_rois_per_image,
+            rois_per_image, self._num_classes)
+
+        if DEBUG:
+            print 'num fg: {}'.format((labels > 0).sum())
+            print 'num bg: {}'.format((labels == 0).sum())
+            self._count += 1
+            self._fg_num += (labels > 0).sum()
+            self._bg_num += (labels == 0).sum()
+            print 'num fg avg: {}'.format(self._fg_num / self._count)
+            print 'num bg avg: {}'.format(self._bg_num / self._count)
+            print 'ratio: {:.3f}'.format(float(self._fg_num) / float(self._bg_num))
+
+        # sampled rois
+        rois = rois.reshape((rois.shape[0], rois.shape[1], 1, 1))
+        top[0].reshape(*rois.shape)
+        top[0].data[...] = rois
+
+        # classification labels
+        labels = labels.reshape((labels.shape[0], 1, 1, 1))
+        top[1].reshape(*labels.shape)
+        top[1].data[...] = labels
+
+        # bbox_targets
+        bbox_targets = bbox_targets.reshape((bbox_targets.shape[0], bbox_targets.shape[1], 1, 1))
+        top[2].reshape(*bbox_targets.shape)
+        top[2].data[...] = bbox_targets
+
+        # bbox_inside_weights
+        bbox_inside_weights = bbox_inside_weights.reshape((bbox_inside_weights.shape[0], bbox_inside_weights.shape[1], 1, 1))
+        top[3].reshape(*bbox_inside_weights.shape)
+        top[3].data[...] = bbox_inside_weights
+
+        # bbox_outside_weights
+        bbox_inside_weights = bbox_inside_weights.reshape((bbox_inside_weights.shape[0], bbox_inside_weights.shape[1], 1, 1))
+        top[4].reshape(*bbox_inside_weights.shape)
+        top[4].data[...] = np.array(bbox_inside_weights > 0).astype(np.float32)
+
+    def backward(self, top, propagate_down, bottom):
+        """This layer does not propagate gradients."""
+        pass
+
+    def reshape(self, bottom, top):
+        """Reshaping happens during the call to forward."""
+        pass
+
+
+def _get_bbox_regression_labels(bbox_target_data, num_classes):
+    """Bounding-box regression targets (bbox_target_data) are stored in a
+    compact form N x (class, tx, ty, tw, th)
+
+    This function expands those targets into the 4-of-4*K representation used
+    by the network (i.e. only one class has non-zero targets).
+
+    Returns:
+        bbox_target (ndarray): N x 4K blob of regression targets
+        bbox_inside_weights (ndarray): N x 4K blob of loss weights
+    """
+
+    clss = bbox_target_data[:, 0]
+    bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
+    bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
+    inds = np.where(clss > 0)[0]
+    if cfg.TRAIN.AGNOSTIC:
+        for ind in inds:
+            cls = clss[ind]
+            start = 4 * (1 if cls > 0 else 0)
+            end = start + 4
+            bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
+            bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
+    else:
+        for ind in inds:
+            cls = clss[ind]
+            start = int(4 * cls)
+            end = int(start + 4)
+            bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
+            bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
+    return bbox_targets, bbox_inside_weights
+
+
+def _compute_targets(ex_rois, gt_rois, labels):
+    """Compute bounding-box regression targets for an image."""
+
+    assert ex_rois.shape[0] == gt_rois.shape[0]
+    assert ex_rois.shape[1] == 4
+    assert gt_rois.shape[1] == 4
+
+    targets = bbox_transform(ex_rois, gt_rois)
+    if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
+        # Optionally normalize targets by a precomputed mean and stdev
+        targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
+                / np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
+    return np.hstack(
+            (labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
+
+def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
+    """Generate a random sample of RoIs comprising foreground and background
+    examples.
+    """
+    # overlaps: (rois x gt_boxes)
+    overlaps = bbox_overlaps(
+        np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
+        np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
+    gt_assignment = overlaps.argmax(axis=1)
+    max_overlaps = overlaps.max(axis=1)
+    labels = gt_boxes[gt_assignment, 4]
+
+    # Select foreground RoIs as those with >= FG_THRESH overlap
+    fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
+    # Guard against the case when an image has fewer than fg_rois_per_image
+    # foreground RoIs
+    fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size)
+    # Sample foreground regions without replacement
+    if fg_inds.size > 0:
+        fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False)
+
+    # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
+    bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
+                       (max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
+    # Compute number of background RoIs to take from this image (guarding
+    # against there being fewer than desired)
+    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
+    bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size)
+    # Sample background regions without replacement
+    if bg_inds.size > 0:
+        bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False)
+
+    # The indices that we're selecting (both fg and bg)
+    keep_inds = np.append(fg_inds, bg_inds)
+    # Select sampled values from various arrays:
+    labels = labels[keep_inds]
+    # Clamp labels for the background RoIs to 0
+    labels[int(fg_rois_per_this_image):] = 0
+    rois = all_rois[keep_inds]
+
+    bbox_target_data = _compute_targets(
+        rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
+
+    bbox_targets, bbox_inside_weights = \
+        _get_bbox_regression_labels(bbox_target_data, num_classes)
+
+    return labels, rois, bbox_targets, bbox_inside_weights
diff --git a/examples/rfcn/lib/setup.py b/examples/rfcn/lib/setup.py
new file mode 100644
index 000000000..df9a0db76
--- /dev/null
+++ b/examples/rfcn/lib/setup.py
@@ -0,0 +1,162 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import os
+from os.path import join as pjoin
+from setuptools import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+import subprocess
+import numpy as np
+
+def find_in_path(name, path):
+    "Find a file in a search path"
+    # Adapted fom
+    # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
+    for dir in path.split(os.pathsep):
+        binpath = pjoin(dir, name)
+        if os.path.exists(binpath):
+            return os.path.abspath(binpath)
+    return None
+
+
+def locate_cuda():
+    """Locate the CUDA environment on the system
+
+    Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
+    and values giving the absolute path to each directory.
+
+    Starts by looking for the CUDAHOME env variable. If not found, everything
+    is based on finding 'nvcc' in the PATH.
+    """
+
+    # first check if the CUDAHOME env variable is in use
+    if 'CUDAHOME' in os.environ:
+        home = os.environ['CUDAHOME']
+        nvcc = pjoin(home, 'bin', 'nvcc')
+    else:
+        # otherwise, search the PATH for NVCC
+        default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
+        nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
+        if nvcc is None:
+            raise EnvironmentError('The nvcc binary could not be '
+                'located in your $PATH. Either add it to your path, or set $CUDAHOME')
+        home = os.path.dirname(os.path.dirname(nvcc))
+
+    cudaconfig = {'home':home, 'nvcc':nvcc,
+                  'include': pjoin(home, 'include'),
+                  'lib64': pjoin(home, 'lib64')}
+    for k, v in cudaconfig.iteritems():
+        if not os.path.exists(v):
+            raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
+
+    return cudaconfig
+#CUDA = locate_cuda()
+
+
+# Obtain the numpy include directory.  This logic works across numpy versions.
+try:
+    numpy_include = np.get_include()
+except AttributeError:
+    numpy_include = np.get_numpy_include()
+
+def customize_compiler_for_nvcc(self):
+    """inject deep into distutils to customize how the dispatch
+    to gcc/nvcc works.
+
+    If you subclass UnixCCompiler, it's not trivial to get your subclass
+    injected in, and still have the right customizations (i.e.
+    distutils.sysconfig.customize_compiler) run on it. So instead of going
+    the OO route, I have this. Note, it's kindof like a wierd functional
+    subclassing going on."""
+
+    # tell the compiler it can processes .cu
+    self.src_extensions.append('.cu')
+
+    # save references to the default compiler_so and _comple methods
+    default_compiler_so = self.compiler_so
+    super = self._compile
+
+    # now redefine the _compile method. This gets executed for each
+    # object but distutils doesn't have the ability to change compilers
+    # based on source extension: we add it.
+    def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
+        if os.path.splitext(src)[1] == '.cu':
+            # use the cuda for .cu files
+            self.set_executable('compiler_so', CUDA['nvcc'])
+            # use only a subset of the extra_postargs, which are 1-1 translated
+            # from the extra_compile_args in the Extension class
+            postargs = extra_postargs['nvcc']
+        else:
+            postargs = extra_postargs['gcc']
+
+        super(obj, src, ext, cc_args, postargs, pp_opts)
+        # reset the default compiler_so, which we might have changed for cuda
+        self.compiler_so = default_compiler_so
+
+    # inject our redefined _compile method into the class
+    self._compile = _compile
+
+
+# run the customize_compiler
+class custom_build_ext(build_ext):
+    def build_extensions(self):
+        customize_compiler_for_nvcc(self.compiler)
+        build_ext.build_extensions(self)
+
+
+ext_modules = [
+    Extension(
+        "utils.cython_mathutils",
+        ["utils/mathutils.pyx"],
+        extra_compile_args={'gcc': ["-O3", "-Wno-cpp", "-Wno-unused-function"]},
+        include_dirs = [numpy_include]
+    ),
+    Extension(
+        "utils.cython_bbox",
+        ["utils/bbox.pyx"],
+        extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
+        include_dirs = [numpy_include]
+    ),
+    Extension(
+        "nms.cpu_nms",
+        ["nms/cpu_nms.pyx"],
+        extra_compile_args={'gcc': ["-O3", "-Wno-cpp", "-Wno-unused-function"]},
+        include_dirs = [numpy_include]
+    ),
+   # Extension('nms.gpu_nms',
+   #     ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'],
+   #     library_dirs=[CUDA['lib64']],
+   #     libraries=['cudart'],
+   #     language='c++',
+   #     runtime_library_dirs=[CUDA['lib64']],
+   #     # this syntax is specific to this build system
+   #     # we're only going to use certain compiler args with nvcc and not with
+   #     # gcc the implementation of this trick is in customize_compiler() below
+   #     extra_compile_args={'gcc': ["-Wno-unused-function"],
+   #                         'nvcc': ['-arch=sm_35',
+   #                                  '--ptxas-options=-v',
+   #                                  '-c',
+   #                                  '--compiler-options',
+   #                                  "'-fPIC'"]},
+   #     include_dirs = [numpy_include, CUDA['include']]
+   # ),
+    Extension(
+        'pycocotools._mask',
+        sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'],
+        include_dirs = [numpy_include, 'pycocotools'],
+        extra_compile_args={
+            'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']},
+    ),
+]
+
+setup(
+    name='fast_rcnn',
+    ext_modules=ext_modules,
+    # inject our custom trigger
+    cmdclass={'build_ext': custom_build_ext},
+)
diff --git a/examples/rfcn/lib/transform/__init__.py b/examples/rfcn/lib/transform/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/rfcn/lib/transform/torch_image_transform_layer.py b/examples/rfcn/lib/transform/torch_image_transform_layer.py
new file mode 100644
index 000000000..9273b3a76
--- /dev/null
+++ b/examples/rfcn/lib/transform/torch_image_transform_layer.py
@@ -0,0 +1,64 @@
+# --------------------------------------------------------
+# Fast/er R-CNN
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+""" Transform images for compatibility with models trained with
+https://github.com/facebook/fb.resnet.torch.
+
+Usage in model prototxt:
+
+layer {
+  name: 'data_xform'
+  type: 'Python'
+  bottom: 'data_caffe'
+  top: 'data'
+  python_param {
+    module: 'transform.torch_image_transform_layer'
+    layer: 'TorchImageTransformLayer'
+  }
+}
+"""
+
+import caffe
+from fast_rcnn.config import cfg
+import numpy as np
+
+class TorchImageTransformLayer(caffe.Layer):
+    def setup(self, bottom, top):
+        # (1, 3, 1, 1) shaped arrays
+        self.PIXEL_MEANS = \
+            np.array([[[[0.48462227599918]],
+                       [[0.45624044862054]],
+                       [[0.40588363755159]]]])
+        self.PIXEL_STDS = \
+            np.array([[[[0.22889466674951]],
+                       [[0.22446679341259]],
+                       [[0.22495548344775]]]])
+        # The default ("old") pixel means that were already subtracted
+        channel_swap = (0, 3, 1, 2)
+        self.OLD_PIXEL_MEANS = \
+            cfg.PIXEL_MEANS[np.newaxis, :, :, :].transpose(channel_swap)
+
+        top[0].reshape(*(bottom[0].shape))
+
+    def forward(self, bottom, top):
+        ims = bottom[0].data
+        # Invert the channel means that were already subtracted
+        ims += self.OLD_PIXEL_MEANS
+        # 1. Permute BGR to RGB and normalize to [0, 1]
+        ims = ims[:, [2, 1, 0], :, :] / 255.0
+        # 2. Remove channel means
+        ims -= self.PIXEL_MEANS
+        # 3. Standardize channels
+        ims /= self.PIXEL_STDS
+        top[0].reshape(*(ims.shape))
+        top[0].data[...] = ims
+
+    def backward(self, top, propagate_down, bottom):
+        """This layer does not propagate gradients."""
+        pass
+
+    def reshape(self, bottom, top):
+        """Reshaping happens during the call to forward."""
+        pass
diff --git a/examples/rfcn/lib/utils/__init__.py b/examples/rfcn/lib/utils/__init__.py
new file mode 100644
index 000000000..7ba6a65c5
--- /dev/null
+++ b/examples/rfcn/lib/utils/__init__.py
@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
diff --git a/examples/rfcn/lib/utils/bbox.pyx b/examples/rfcn/lib/utils/bbox.pyx
new file mode 100644
index 000000000..e14780ddd
--- /dev/null
+++ b/examples/rfcn/lib/utils/bbox.pyx
@@ -0,0 +1,55 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Sergey Karayev
+# --------------------------------------------------------
+
+cimport cython
+import numpy as np
+cimport numpy as np
+
+DTYPE = np.float
+ctypedef np.float_t DTYPE_t
+
+def bbox_overlaps(
+        np.ndarray[DTYPE_t, ndim=2] boxes,
+        np.ndarray[DTYPE_t, ndim=2] query_boxes):
+    """
+    Parameters
+    ----------
+    boxes: (N, 4) ndarray of float
+    query_boxes: (K, 4) ndarray of float
+    Returns
+    -------
+    overlaps: (N, K) ndarray of overlap between boxes and query_boxes
+    """
+    cdef unsigned int N = boxes.shape[0]
+    cdef unsigned int K = query_boxes.shape[0]
+    cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE)
+    cdef DTYPE_t iw, ih, box_area
+    cdef DTYPE_t ua
+    cdef unsigned int k, n
+    for k in range(K):
+        box_area = (
+            (query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+            (query_boxes[k, 3] - query_boxes[k, 1] + 1)
+        )
+        for n in range(N):
+            iw = (
+                min(boxes[n, 2], query_boxes[k, 2]) -
+                max(boxes[n, 0], query_boxes[k, 0]) + 1
+            )
+            if iw > 0:
+                ih = (
+                    min(boxes[n, 3], query_boxes[k, 3]) -
+                    max(boxes[n, 1], query_boxes[k, 1]) + 1
+                )
+                if ih > 0:
+                    ua = float(
+                        (boxes[n, 2] - boxes[n, 0] + 1) *
+                        (boxes[n, 3] - boxes[n, 1] + 1) +
+                        box_area - iw * ih
+                    )
+                    overlaps[n, k] = iw * ih / ua
+    return overlaps
diff --git a/examples/rfcn/lib/utils/blob.py b/examples/rfcn/lib/utils/blob.py
new file mode 100644
index 000000000..249d179f0
--- /dev/null
+++ b/examples/rfcn/lib/utils/blob.py
@@ -0,0 +1,48 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Blob helper functions."""
+
+import numpy as np
+import cv2
+from utils.cython_mathutils import cpu_subtract
+
+def im_list_to_blob(ims):
+    """Convert a list of images into a network input.
+
+    Assumes images are already prepared (means subtracted, BGR order, ...).
+    """
+    max_shape = np.array([im.shape for im in ims]).max(axis=0)
+    num_images = len(ims)
+    blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
+                    dtype=np.float32)
+    for i in xrange(num_images):
+        im = ims[i]
+        blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
+    # Move channels (axis 3) to axis 1
+    # Axis order will become: (batch elem, channel, height, width)
+    channel_swap = (0, 3, 1, 2)
+    blob = blob.transpose(channel_swap)
+    return blob
+
+def prep_im_for_blob(im, pixel_means, target_size, max_size):
+    """Mean subtract and scale an image for use in a blob."""
+    im = im.astype(np.float32, copy=False)
+    pixel_means = pixel_means.astype(np.float32, copy=False)
+    # im -= pixel_means
+    cpu_subtract(im, pixel_means)
+    im_shape = im.shape
+    im_size_min = np.min(im_shape[0:2])
+    im_size_max = np.max(im_shape[0:2])
+    im_scale = float(target_size) / float(im_size_min)
+    # Prevent the biggest axis from being more than MAX_SIZE
+    if np.round(im_scale * im_size_max) > max_size:
+        im_scale = float(max_size) / float(im_size_max)
+    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
+                    interpolation=cv2.INTER_LINEAR)
+
+    return im, im_scale
diff --git a/examples/rfcn/lib/utils/mathutils.pyx b/examples/rfcn/lib/utils/mathutils.pyx
new file mode 100644
index 000000000..13f410d9a
--- /dev/null
+++ b/examples/rfcn/lib/utils/mathutils.pyx
@@ -0,0 +1,41 @@
+# --------------------------------------------------------
+# Math Utils
+# Copyright (c) 2017 Intel
+# Licensed under The MIT License [see LICENSE for details]
+# Written by YAO Matrix
+# --------------------------------------------------------
+
+import numpy as np
+cimport numpy as np
+cimport cython
+cimport openmp
+
+from cython.parallel cimport prange
+from cython.parallel cimport parallel
+
+import os
+from multiprocessing import cpu_count
+cdef int thread_num = 0
+try:
+    thread_num = os.environ["OMP_NUM_THREADS"]
+except:
+    thread_num = cpu_count() / 2
+
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def cpu_subtract(np.ndarray[np.float32_t, ndim=3] src, np.ndarray[np.float32_t, ndim=3] scalar):
+    global thread_num
+
+    cdef unsigned int rows = src.shape[0]
+    cdef unsigned int cols = src.shape[1]
+    cdef unsigned int chs = src.shape[2]
+
+    cdef int i, j, k
+
+    with nogil:
+        for i in prange(rows, schedule = 'dynamic', num_threads = thread_num):
+            for j in xrange(cols):
+                for k in xrange(chs):
+                    src[i, j, k] -= scalar[0, 0, k]
diff --git a/examples/rfcn/lib/utils/timer.py b/examples/rfcn/lib/utils/timer.py
new file mode 100644
index 000000000..dacc942c2
--- /dev/null
+++ b/examples/rfcn/lib/utils/timer.py
@@ -0,0 +1,32 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import time
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        self.total_time += self.diff
+        self.calls += 1
+        self.average_time = self.total_time / self.calls
+        if average:
+            return self.average_time
+        else:
+            return self.diff
diff --git a/examples/rfcn/run.sh b/examples/rfcn/run.sh
new file mode 100755
index 000000000..4f1d536e5
--- /dev/null
+++ b/examples/rfcn/run.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
+export GLOG_minloglevel=0
+
+
+unset OMP_NUM_THREADS
+# export OMP_NUM_THREADS=44
+# export MKL_NUM_THREADS
+
+# export OMP_WAIT_POLICY=passive
+unset MKL_THREADING_LAYER
+# export MKL_THREADING_LAYER=gnu
+
+
+./experiments/scripts/rfcn_end2end.sh 0 ResNet-101 pascal_voc
diff --git a/examples/rfcn/tools/README.md b/examples/rfcn/tools/README.md
new file mode 100644
index 000000000..6fd409412
--- /dev/null
+++ b/examples/rfcn/tools/README.md
@@ -0,0 +1 @@
+Tools for training, testing, and compressing Fast R-CNN networks.
diff --git a/examples/rfcn/tools/_init_paths.py b/examples/rfcn/tools/_init_paths.py
new file mode 100755
index 000000000..20577f0d7
--- /dev/null
+++ b/examples/rfcn/tools/_init_paths.py
@@ -0,0 +1,26 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Set up paths for Fast R-CNN."""
+
+import os.path as osp
+import sys
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+this_dir = osp.dirname(__file__)
+
+# Add caffe to PYTHONPATH
+#caffe_path = osp.join(this_dir, '..', 'caffe-fast-rcnn', 'python')
+caffe_path = osp.join(this_dir, '..', '..', '..', 'python')
+add_path(caffe_path)
+
+# Add lib to PYTHONPATH
+lib_path = osp.join(this_dir, '..', 'lib')
+add_path(lib_path)
diff --git a/examples/rfcn/tools/compress_net.py b/examples/rfcn/tools/compress_net.py
new file mode 100755
index 000000000..e044e5bcd
--- /dev/null
+++ b/examples/rfcn/tools/compress_net.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Compress a Fast R-CNN network using truncated SVD."""
+
+import _init_paths
+import caffe
+import argparse
+import numpy as np
+import os, sys
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Compress a Fast R-CNN network')
+    parser.add_argument('--def', dest='prototxt',
+                        help='prototxt file defining the uncompressed network',
+                        default=None, type=str)
+    parser.add_argument('--def-svd', dest='prototxt_svd',
+                        help='prototxt file defining the SVD compressed network',
+                        default=None, type=str)
+    parser.add_argument('--net', dest='caffemodel',
+                        help='model to compress',
+                        default=None, type=str)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+def compress_weights(W, l):
+    """Compress the weight matrix W of an inner product (fully connected) layer
+    using truncated SVD.
+
+    Parameters:
+    W: N x M weights matrix
+    l: number of singular values to retain
+
+    Returns:
+    Ul, L: matrices such that W \approx Ul*L
+    """
+
+    # numpy doesn't seem to have a fast truncated SVD algorithm...
+    # this could be faster
+    U, s, V = np.linalg.svd(W, full_matrices=False)
+
+    Ul = U[:, :l]
+    sl = s[:l]
+    Vl = V[:l, :]
+
+    L = np.dot(np.diag(sl), Vl)
+    return Ul, L
+
+def main():
+    args = parse_args()
+
+    # prototxt = 'models/VGG16/test.prototxt'
+    # caffemodel = 'snapshots/vgg16_fast_rcnn_iter_40000.caffemodel'
+    net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
+
+    # prototxt_svd = 'models/VGG16/svd/test_fc6_fc7.prototxt'
+    # caffemodel = 'snapshots/vgg16_fast_rcnn_iter_40000.caffemodel'
+    net_svd = caffe.Net(args.prototxt_svd, args.caffemodel, caffe.TEST)
+
+    print('Uncompressed network {} : {}'.format(args.prototxt, args.caffemodel))
+    print('Compressed network prototxt {}'.format(args.prototxt_svd))
+
+    out = os.path.splitext(os.path.basename(args.caffemodel))[0] + '_svd'
+    out_dir = os.path.dirname(args.caffemodel)
+
+    # Compress fc6
+    if net_svd.params.has_key('fc6_L'):
+        l_fc6 = net_svd.params['fc6_L'][0].data.shape[0]
+        print('  fc6_L bottleneck size: {}'.format(l_fc6))
+
+        # uncompressed weights and biases
+        W_fc6 = net.params['fc6'][0].data
+        B_fc6 = net.params['fc6'][1].data
+
+        print('  compressing fc6...')
+        Ul_fc6, L_fc6 = compress_weights(W_fc6, l_fc6)
+
+        assert(len(net_svd.params['fc6_L']) == 1)
+
+        # install compressed matrix factors (and original biases)
+        net_svd.params['fc6_L'][0].data[...] = L_fc6
+
+        net_svd.params['fc6_U'][0].data[...] = Ul_fc6
+        net_svd.params['fc6_U'][1].data[...] = B_fc6
+
+        out += '_fc6_{}'.format(l_fc6)
+
+    # Compress fc7
+    if net_svd.params.has_key('fc7_L'):
+        l_fc7 = net_svd.params['fc7_L'][0].data.shape[0]
+        print '  fc7_L bottleneck size: {}'.format(l_fc7)
+
+        W_fc7 = net.params['fc7'][0].data
+        B_fc7 = net.params['fc7'][1].data
+
+        print('  compressing fc7...')
+        Ul_fc7, L_fc7 = compress_weights(W_fc7, l_fc7)
+
+        assert(len(net_svd.params['fc7_L']) == 1)
+
+        net_svd.params['fc7_L'][0].data[...] = L_fc7
+
+        net_svd.params['fc7_U'][0].data[...] = Ul_fc7
+        net_svd.params['fc7_U'][1].data[...] = B_fc7
+
+        out += '_fc7_{}'.format(l_fc7)
+
+    filename = '{}/{}.caffemodel'.format(out_dir, out)
+    net_svd.save(filename)
+    print 'Wrote svd model to: {:s}'.format(filename)
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/rfcn/tools/demo.py b/examples/rfcn/tools/demo.py
new file mode 100755
index 000000000..7293b4635
--- /dev/null
+++ b/examples/rfcn/tools/demo.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""
+Demo script showing detections in sample images.
+
+See README.md for installation instructions before running.
+"""
+
+import _init_paths
+from fast_rcnn.config import cfg
+from fast_rcnn.test import im_detect
+from fast_rcnn.nms_wrapper import nms
+from utils.timer import Timer
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.io as sio
+import caffe, os, sys, cv2
+import argparse
+
+CLASSES = ('__background__',
+           'aeroplane', 'bicycle', 'bird', 'boat',
+           'bottle', 'bus', 'car', 'cat', 'chair',
+           'cow', 'diningtable', 'dog', 'horse',
+           'motorbike', 'person', 'pottedplant',
+           'sheep', 'sofa', 'train', 'tvmonitor')
+
+NETS = {'vgg16': ('VGG16',
+                  'VGG16_faster_rcnn_final.caffemodel'),
+        'zf': ('ZF',
+                  'ZF_faster_rcnn_final.caffemodel')}
+
+
+def vis_detections(im, class_name, dets, thresh=0.5):
+    """Draw detected bounding boxes."""
+    inds = np.where(dets[:, -1] >= thresh)[0]
+    if len(inds) == 0:
+        return
+
+    im = im[:, :, (2, 1, 0)]
+    fig, ax = plt.subplots(figsize=(12, 12))
+    ax.imshow(im, aspect='equal')
+    for i in inds:
+        bbox = dets[i, :4]
+        score = dets[i, -1]
+
+        ax.add_patch(
+            plt.Rectangle((bbox[0], bbox[1]),
+                          bbox[2] - bbox[0],
+                          bbox[3] - bbox[1], fill=False,
+                          edgecolor='red', linewidth=3.5)
+            )
+        ax.text(bbox[0], bbox[1] - 2,
+                '{:s} {:.3f}'.format(class_name, score),
+                bbox=dict(facecolor='blue', alpha=0.5),
+                fontsize=14, color='white')
+
+    ax.set_title(('{} detections with '
+                  'p({} | box) >= {:.1f}').format(class_name, class_name,
+                                                  thresh),
+                  fontsize=14)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.draw()
+
+def demo(net, image_name):
+    """Detect object classes in an image using pre-computed object proposals."""
+
+    # Load the demo image
+    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
+    im = cv2.imread(im_file)
+
+    # Detect all object classes and regress object bounds
+    timer = Timer()
+    timer.tic()
+    scores, boxes = im_detect(net, im)
+    timer.toc()
+    print ('Detection took {:.3f}s for '
+           '{:d} object proposals').format(timer.total_time, boxes.shape[0])
+
+    # Visualize detections for each class
+    CONF_THRESH = 0.8
+    NMS_THRESH = 0.3
+    for cls_ind, cls in enumerate(CLASSES[1:]):
+        cls_ind += 1 # because we skipped background
+        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
+        cls_scores = scores[:, cls_ind]
+        dets = np.hstack((cls_boxes,
+                          cls_scores[:, np.newaxis])).astype(np.float32)
+        keep = nms(dets, NMS_THRESH)
+        dets = dets[keep, :]
+        vis_detections(im, cls, dets, thresh=CONF_THRESH)
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
+    parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
+                        choices=NETS.keys(), default='vgg16')
+
+    args = parser.parse_args()
+
+    return args
+
+if __name__ == '__main__':
+    cfg.TEST.HAS_RPN = True  # Use RPN for proposals
+
+    args = parse_args()
+
+    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
+                            'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
+    caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
+                              NETS[args.demo_net][1])
+
+    if not os.path.isfile(caffemodel):
+        raise IOError(('{:s} not found.\nDid you run ./data/script/'
+                       'fetch_faster_rcnn_models.sh?').format(caffemodel))
+
+    caffe.set_mode_cpu()
+    net = caffe.Net(prototxt, caffemodel, caffe.TEST)
+
+    print '\n\nLoaded network {:s}'.format(caffemodel)
+
+    # Warmup on a dummy image
+    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
+    for i in xrange(2):
+        _, _= im_detect(net, im)
+
+    im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
+                '001763.jpg', '004545.jpg']
+    for im_name in im_names:
+        print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+        print 'Demo for data/demo/{}'.format(im_name)
+        demo(net, im_name)
+
+    plt.show()
diff --git a/examples/rfcn/tools/demo_rfcn.py b/examples/rfcn/tools/demo_rfcn.py
new file mode 100755
index 000000000..cece47846
--- /dev/null
+++ b/examples/rfcn/tools/demo_rfcn.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# R-FCN
+# Copyright (c) 2016 Yuwen Xiong
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Yuwen Xiong
+# --------------------------------------------------------
+
+"""
+Demo script showing detections in sample images.
+
+See README.md for installation instructions before running.
+"""
+
+import _init_paths
+from fast_rcnn.config import cfg
+from fast_rcnn.test import im_detect
+from fast_rcnn.nms_wrapper import nms
+from utils.timer import Timer
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.io as sio
+import caffe, os, sys, cv2
+import argparse
+
+CLASSES = ('__background__',
+           'aeroplane', 'bicycle', 'bird', 'boat',
+           'bottle', 'bus', 'car', 'cat', 'chair',
+           'cow', 'diningtable', 'dog', 'horse',
+           'motorbike', 'person', 'pottedplant',
+           'sheep', 'sofa', 'train', 'tvmonitor')
+
+NETS = {'ResNet-101': ('ResNet-101',
+                  'resnet101_rfcn_final.caffemodel'),
+        'ResNet-50': ('ResNet-50',
+                  'resnet50_rfcn_final.caffemodel')}
+
+
+def vis_detections(im, class_name, dets, thresh=0.5):
+    """Draw detected bounding boxes."""
+    inds = np.where(dets[:, -1] >= thresh)[0]
+    if len(inds) == 0:
+        return
+
+    im = im[:, :, (2, 1, 0)]
+    fig, ax = plt.subplots(figsize=(12, 12))
+    ax.imshow(im, aspect='equal')
+    for i in inds:
+        bbox = dets[i, :4]
+        score = dets[i, -1]
+
+        ax.add_patch(
+            plt.Rectangle((bbox[0], bbox[1]),
+                          bbox[2] - bbox[0],
+                          bbox[3] - bbox[1], fill=False,
+                          edgecolor='red', linewidth=3.5)
+            )
+        ax.text(bbox[0], bbox[1] - 2,
+                '{:s} {:.3f}'.format(class_name, score),
+                bbox=dict(facecolor='blue', alpha=0.5),
+                fontsize=14, color='white')
+
+    ax.set_title(('{} detections with '
+                  'p({} | box) >= {:.1f}').format(class_name, class_name,
+                                                  thresh),
+                  fontsize=14)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.draw()
+
+def demo(net, image_name):
+    """Detect object classes in an image using pre-computed object proposals."""
+
+    # Load the demo image
+    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
+    im = cv2.imread(im_file)
+    # print im.shape
+
+    # Detect all object classes and regress object bounds
+    timer = Timer()
+    timer.tic()
+    scores, boxes = im_detect(net, im)
+    timer.toc()
+    print ('Detection took {:.3f}s for '
+           '{:d} object proposals').format(timer.total_time, boxes.shape[0])
+
+    # Visualize detections for each class
+    CONF_THRESH = 0.8
+    NMS_THRESH = 0.3
+    for cls_ind, cls in enumerate(CLASSES[1:]):
+        cls_ind += 1 # because we skipped background
+        cls_boxes = boxes[:, 4:8]
+        cls_scores = scores[:, cls_ind]
+        dets = np.hstack((cls_boxes,
+                          cls_scores[:, np.newaxis])).astype(np.float32)
+        keep = nms(dets, NMS_THRESH)
+        dets = dets[keep, :]
+        # print dets
+        vis_detections(im, cls, dets, thresh=CONF_THRESH)
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
+                        default=0, type=int)
+    parser.add_argument('--cpu', dest='cpu_mode',
+                        help='Use CPU mode (overrides --gpu)',
+                        action='store_true')
+    parser.add_argument('--net', dest='demo_net', help='Network to use [ResNet-101]',
+                        choices=NETS.keys(), default='ResNet-101')
+
+    args = parser.parse_args()
+
+    return args
+
+if __name__ == '__main__':
+    cfg.TEST.HAS_RPN = True  # Use RPN for proposals
+
+    args = parse_args()
+
+    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
+                            'rfcn_end2end', 'test_agnostic.prototxt')
+    caffemodel = os.path.join(cfg.DATA_DIR, 'rfcn_models',
+                              NETS[args.demo_net][1])
+
+    if not os.path.isfile(caffemodel):
+        raise IOError(('{:s} not found.\n').format(caffemodel))
+    args.cpu_mode = 1 
+    if args.cpu_mode:
+        caffe.set_mode_cpu()
+    else:
+        caffe.set_mode_gpu()
+        caffe.set_device(args.gpu_id)
+        cfg.GPU_ID = args.gpu_id
+
+    print(prototxt)
+    net = caffe.Net(prototxt, caffemodel, caffe.TEST)
+
+    print '\n\nLoaded network {:s}'.format(caffemodel)
+    import time
+    time.sleep(5)
+
+    # Warmup on a dummy image
+    im = 128 * np.ones((375, 500, 3), dtype=np.uint8)
+    # im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
+    print '\n dummy image generation done' 
+    for i in xrange(2):
+      print '\n start object detection'
+      _, _= im_detect(net, im)
+      print '\n object detection done'
+
+    im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
+                '001763.jpg', '004545.jpg']
+    for im_name in im_names:
+        print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+        print 'Demo for data/demo/{}'.format(im_name)
+        demo(net, im_name)
+
+    plt.show()
diff --git a/examples/rfcn/tools/eval_recall.py b/examples/rfcn/tools/eval_recall.py
new file mode 100755
index 000000000..b1a59dc27
--- /dev/null
+++ b/examples/rfcn/tools/eval_recall.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+
+import _init_paths
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list
+from datasets.factory import get_imdb
+import argparse
+import time, os, sys
+import numpy as np
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to test',
+                        default='voc_2007_test', type=str)
+    parser.add_argument('--method', dest='method',
+                        help='proposal method',
+                        default='selective_search', type=str)
+    parser.add_argument('--rpn-file', dest='rpn_file',
+                        default=None, type=str)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    imdb = get_imdb(args.imdb_name)
+    imdb.set_proposal_method(args.method)
+    if args.rpn_file is not None:
+        imdb.config['rpn_file'] = args.rpn_file
+
+    candidate_boxes = None
+    if 0:
+        import scipy.io as sio
+        filename = 'debug/stage1_rpn_voc_2007_test.mat'
+        raw_data = sio.loadmat(filename)['aboxes'].ravel()
+        candidate_boxes = raw_data
+
+    ar, gt_overlaps, recalls, thresholds = \
+        imdb.evaluate_recall(candidate_boxes=candidate_boxes)
+    print 'Method: {}'.format(args.method)
+    print 'AverageRec: {:.3f}'.format(ar)
+
+    def recall_at(t):
+        ind = np.where(thresholds > t - 1e-5)[0][0]
+        assert np.isclose(thresholds[ind], t)
+        return recalls[ind]
+
+    print 'Recall@0.5: {:.3f}'.format(recall_at(0.5))
+    print 'Recall@0.6: {:.3f}'.format(recall_at(0.6))
+    print 'Recall@0.7: {:.3f}'.format(recall_at(0.7))
+    print 'Recall@0.8: {:.3f}'.format(recall_at(0.8))
+    print 'Recall@0.9: {:.3f}'.format(recall_at(0.9))
+    # print again for easy spreadsheet copying
+    print '{:.3f}'.format(ar)
+    print '{:.3f}'.format(recall_at(0.5))
+    print '{:.3f}'.format(recall_at(0.6))
+    print '{:.3f}'.format(recall_at(0.7))
+    print '{:.3f}'.format(recall_at(0.8))
+    print '{:.3f}'.format(recall_at(0.9))
diff --git a/examples/rfcn/tools/reval.py b/examples/rfcn/tools/reval.py
new file mode 100755
index 000000000..905ec1b14
--- /dev/null
+++ b/examples/rfcn/tools/reval.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Reval = re-eval. Re-evaluate saved detections."""
+
+import _init_paths
+from fast_rcnn.test import apply_nms
+from fast_rcnn.config import cfg
+from datasets.factory import get_imdb
+import cPickle
+import os, sys, argparse
+import numpy as np
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Re-evaluate results')
+    parser.add_argument('output_dir', nargs=1, help='results directory',
+                        type=str)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to re-evaluate',
+                        default='voc_2007_test', type=str)
+    parser.add_argument('--matlab', dest='matlab_eval',
+                        help='use matlab for evaluation',
+                        action='store_true')
+    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
+                        action='store_true')
+    parser.add_argument('--nms', dest='apply_nms', help='apply nms',
+                        action='store_true')
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+def from_dets(imdb_name, output_dir, args):
+    imdb = get_imdb(imdb_name)
+    imdb.competition_mode(args.comp_mode)
+    imdb.config['matlab_eval'] = args.matlab_eval
+    with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
+        dets = cPickle.load(f)
+
+    if args.apply_nms:
+        print 'Applying NMS to all detections'
+        nms_dets = apply_nms(dets, cfg.TEST.NMS)
+    else:
+        nms_dets = dets
+
+    print 'Evaluating detections'
+    imdb.evaluate_detections(nms_dets, output_dir)
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    output_dir = os.path.abspath(args.output_dir[0])
+    imdb_name = args.imdb_name
+    from_dets(imdb_name, output_dir, args)
diff --git a/examples/rfcn/tools/rpn_generate.py b/examples/rfcn/tools/rpn_generate.py
new file mode 100755
index 000000000..f8ca4a167
--- /dev/null
+++ b/examples/rfcn/tools/rpn_generate.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast/er/ R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Generate RPN proposals."""
+
+import _init_paths
+import numpy as np
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
+from datasets.factory import get_imdb
+from rpn.generate import imdb_proposals
+import cPickle
+import caffe
+import argparse
+import pprint
+import time, os, sys
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
+                        default=0, type=int)
+    parser.add_argument('--def', dest='prototxt',
+                        help='prototxt file defining the network',
+                        default=None, type=str)
+    parser.add_argument('--net', dest='caffemodel',
+                        help='model to test',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file', default=None, type=str)
+    parser.add_argument('--wait', dest='wait',
+                        help='wait until net file exists',
+                        default=True, type=bool)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to test',
+                        default='voc_2007_test', type=str)
+    parser.add_argument('--set', dest='set_cfgs',
+                        help='set config keys', default=None,
+                        nargs=argparse.REMAINDER)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+
+    cfg.GPU_ID = args.gpu_id
+
+    # RPN test settings
+    cfg.TEST.RPN_PRE_NMS_TOP_N = -1
+    cfg.TEST.RPN_POST_NMS_TOP_N = 2000
+
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    while not os.path.exists(args.caffemodel) and args.wait:
+        print('Waiting for {} to exist...'.format(args.caffemodel))
+        time.sleep(10)
+
+    caffe.set_mode_gpu()
+    caffe.set_device(args.gpu_id)
+    net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
+    net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]
+
+    imdb = get_imdb(args.imdb_name)
+    imdb_boxes = imdb_proposals(net, imdb)
+
+    output_dir = get_output_dir(imdb, net)
+    rpn_file = os.path.join(output_dir, net.name + '_rpn_proposals.pkl')
+    with open(rpn_file, 'wb') as f:
+        cPickle.dump(imdb_boxes, f, cPickle.HIGHEST_PROTOCOL)
+    print 'Wrote RPN proposals to {}'.format(rpn_file)
diff --git a/examples/rfcn/tools/test_net.py b/examples/rfcn/tools/test_net.py
new file mode 100755
index 000000000..ff47037d6
--- /dev/null
+++ b/examples/rfcn/tools/test_net.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Test a Fast R-CNN network on an image database."""
+
+import _init_paths
+from fast_rcnn.test import test_net
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list
+from datasets.factory import get_imdb
+import caffe
+import argparse
+import pprint
+import time, os, sys
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Test a Fast R-CNN network')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
+                        default=0, type=int)
+    parser.add_argument('--def', dest='prototxt',
+                        help='prototxt file defining the network',
+                        default=None, type=str)
+    parser.add_argument('--net', dest='caffemodel',
+                        help='model to test',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file', default=None, type=str)
+    parser.add_argument('--wait', dest='wait',
+                        help='wait until net file exists',
+                        default=True, type=bool)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to test',
+                        default='voc_2007_test', type=str)
+    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
+                        action='store_true')
+    parser.add_argument('--set', dest='set_cfgs',
+                        help='set config keys', default=None,
+                        nargs=argparse.REMAINDER)
+    parser.add_argument('--vis', dest='vis', help='visualize detections',
+                        action='store_true')
+    parser.add_argument('--num_dets', dest='max_per_image',
+                        help='max number of detections per image',
+                        default=400, type=int)
+    parser.add_argument('--rpn_file', dest='rpn_file',
+                        default=None, type=str)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+
+    cfg.GPU_ID = args.gpu_id
+
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    while not os.path.exists(args.caffemodel) and args.wait:
+        print('Waiting for {} to exist...'.format(args.caffemodel))
+        time.sleep(10)
+
+    #caffe.set_mode_gpu()
+    #caffe.set_device(args.gpu_id)
+    net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
+    net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]
+
+    imdb = get_imdb(args.imdb_name)
+    imdb.competition_mode(args.comp_mode)
+    if not cfg.TEST.HAS_RPN:
+        imdb.set_proposal_method(cfg.TEST.PROPOSAL_METHOD)
+        if cfg.TEST.PROPOSAL_METHOD == 'rpn':
+            imdb.config['rpn_file'] = args.rpn_file
+
+    test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis)
diff --git a/examples/rfcn/tools/train_faster_rcnn_alt_opt.py b/examples/rfcn/tools/train_faster_rcnn_alt_opt.py
new file mode 100755
index 000000000..e49844a45
--- /dev/null
+++ b/examples/rfcn/tools/train_faster_rcnn_alt_opt.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Faster R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Train a Faster R-CNN network using alternating optimization.
+This tool implements the alternating optimization algorithm described in our
+NIPS 2015 paper ("Faster R-CNN: Towards Real-time Object Detection with Region
+Proposal Networks." Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.)
+"""
+
+import _init_paths
+from fast_rcnn.train import get_training_roidb, train_net
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
+from datasets.factory import get_imdb
+from rpn.generate import imdb_proposals
+import argparse
+import pprint
+import numpy as np
+import sys, os
+import multiprocessing as mp
+import cPickle
+import shutil
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Train a Faster R-CNN network')
+    parser.add_argument('--gpu', dest='gpu_id',
+                        help='GPU device id to use [0]',
+                        default=0, type=int)
+    parser.add_argument('--net_name', dest='net_name',
+                        help='network name (e.g., "ZF")',
+                        default=None, type=str)
+    parser.add_argument('--weights', dest='pretrained_model',
+                        help='initialize with pretrained model weights',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file',
+                        default=None, type=str)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to train on',
+                        default='voc_2007_trainval', type=str)
+    parser.add_argument('--set', dest='set_cfgs',
+                        help='set config keys', default=None,
+                        nargs=argparse.REMAINDER)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+def get_roidb(imdb_name, rpn_file=None):
+    imdb = get_imdb(imdb_name)
+    print 'Loaded dataset `{:s}` for training'.format(imdb.name)
+    imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
+    print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
+    if rpn_file is not None:
+        imdb.config['rpn_file'] = rpn_file
+    roidb = get_training_roidb(imdb)
+    return roidb, imdb
+
+def get_solvers(net_name):
+    # Faster R-CNN Alternating Optimization
+    n = 'faster_rcnn_alt_opt'
+    # Solver for each training stage
+    solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
+               [net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
+               [net_name, n, 'stage2_rpn_solver60k80k.pt'],
+               [net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
+    solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
+    # Iterations for each training stage
+    max_iters = [80000, 40000, 80000, 40000]
+    # max_iters = [100, 100, 100, 100]
+    # Test prototxt for the RPN
+    rpn_test_prototxt = os.path.join(
+        cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
+    return solvers, max_iters, rpn_test_prototxt
+
+# ------------------------------------------------------------------------------
+# Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
+# (e.g. "del net" in Python code). To work around this issue, each training
+# stage is executed in a separate process using multiprocessing.Process.
+# ------------------------------------------------------------------------------
+
+def _init_caffe(cfg):
+    """Initialize pycaffe in a training process.
+    """
+
+    import caffe
+    # fix the random seeds (numpy and caffe) for reproducibility
+    np.random.seed(cfg.RNG_SEED)
+    caffe.set_random_seed(cfg.RNG_SEED)
+    # set up caffe
+    caffe.set_mode_gpu()
+    caffe.set_device(cfg.GPU_ID)
+
+def train_rpn(queue=None, imdb_name=None, init_model=None, solver=None,
+              max_iters=None, cfg=None):
+    """Train a Region Proposal Network in a separate training process.
+    """
+
+    # Not using any proposals, just ground-truth boxes
+    cfg.TRAIN.HAS_RPN = True
+    cfg.TRAIN.BBOX_REG = False  # applies only to Fast R-CNN bbox regression
+    cfg.TRAIN.PROPOSAL_METHOD = 'gt'
+    cfg.TRAIN.IMS_PER_BATCH = 1
+    print 'Init model: {}'.format(init_model)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    roidb, imdb = get_roidb(imdb_name)
+    print 'roidb len: {}'.format(len(roidb))
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+
+    model_paths = train_net(solver, roidb, output_dir,
+                            pretrained_model=init_model,
+                            max_iters=max_iters)
+    # Cleanup all but the final model
+    for i in model_paths[:-1]:
+        os.remove(i)
+    rpn_model_path = model_paths[-1]
+    # Send final model path through the multiprocessing queue
+    queue.put({'model_path': rpn_model_path})
+
+def rpn_generate(queue=None, imdb_name=None, rpn_model_path=None, cfg=None,
+                 rpn_test_prototxt=None):
+    """Use a trained RPN to generate proposals.
+    """
+
+    cfg.TEST.RPN_PRE_NMS_TOP_N = -1     # no pre NMS filtering
+    cfg.TEST.RPN_POST_NMS_TOP_N = 2000  # limit top boxes after NMS
+    print 'RPN model: {}'.format(rpn_model_path)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    # NOTE: the matlab implementation computes proposals on flipped images, too.
+    # We compute them on the image once and then flip the already computed
+    # proposals. This might cause a minor loss in mAP (less proposal jittering).
+    imdb = get_imdb(imdb_name)
+    print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name)
+
+    # Load RPN and configure output directory
+    rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST)
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+    # Generate proposals on the imdb
+    rpn_proposals = imdb_proposals(rpn_net, imdb)
+    # Write proposals to disk and send the proposal file path through the
+    # multiprocessing queue
+    rpn_net_name = os.path.splitext(os.path.basename(rpn_model_path))[0]
+    rpn_proposals_path = os.path.join(
+        output_dir, rpn_net_name + '_proposals.pkl')
+    with open(rpn_proposals_path, 'wb') as f:
+        cPickle.dump(rpn_proposals, f, cPickle.HIGHEST_PROTOCOL)
+    print 'Wrote RPN proposals to {}'.format(rpn_proposals_path)
+    queue.put({'proposal_path': rpn_proposals_path})
+
+def train_fast_rcnn(queue=None, imdb_name=None, init_model=None, solver=None,
+                    max_iters=None, cfg=None, rpn_file=None):
+    """Train a Fast R-CNN using proposals generated by an RPN.
+    """
+
+    cfg.TRAIN.HAS_RPN = False           # not generating prosals on-the-fly
+    cfg.TRAIN.PROPOSAL_METHOD = 'rpn'   # use pre-computed RPN proposals instead
+    cfg.TRAIN.IMS_PER_BATCH = 2
+    print 'Init model: {}'.format(init_model)
+    print 'RPN proposals: {}'.format(rpn_file)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    roidb, imdb = get_roidb(imdb_name, rpn_file=rpn_file)
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+    # Train Fast R-CNN
+    model_paths = train_net(solver, roidb, output_dir,
+                            pretrained_model=init_model,
+                            max_iters=max_iters)
+    # Cleanup all but the final model
+    for i in model_paths[:-1]:
+        os.remove(i)
+    fast_rcnn_model_path = model_paths[-1]
+    # Send Fast R-CNN model path over the multiprocessing queue
+    queue.put({'model_path': fast_rcnn_model_path})
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+    cfg.GPU_ID = args.gpu_id
+
+    # --------------------------------------------------------------------------
+    # Pycaffe doesn't reliably free GPU memory when instantiated nets are
+    # discarded (e.g. "del net" in Python code). To work around this issue, each
+    # training stage is executed in a separate process using
+    # multiprocessing.Process.
+    # --------------------------------------------------------------------------
+
+    # queue for communicated results between processes
+    mp_queue = mp.Queue()
+    # solves, iters, etc. for each training stage
+    solvers, max_iters, rpn_test_prototxt = get_solvers(args.net_name)
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 RPN, init from ImageNet model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=args.pretrained_model,
+            solver=solvers[0],
+            max_iters=max_iters[0],
+            cfg=cfg)
+    p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage1_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 RPN, generate proposals'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            rpn_model_path=str(rpn_stage1_out['model_path']),
+            cfg=cfg,
+            rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage1_out['proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 Fast R-CNN using RPN proposals, init from ImageNet model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=args.pretrained_model,
+            solver=solvers[1],
+            max_iters=max_iters[1],
+            cfg=cfg,
+            rpn_file=rpn_stage1_out['proposal_path'])
+    p = mp.Process(target=train_fast_rcnn, kwargs=mp_kwargs)
+    p.start()
+    fast_rcnn_stage1_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 RPN, init from stage 1 Fast R-CNN model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=str(fast_rcnn_stage1_out['model_path']),
+            solver=solvers[2],
+            max_iters=max_iters[2],
+            cfg=cfg)
+    p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage2_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 RPN, generate proposals'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            rpn_model_path=str(rpn_stage2_out['model_path']),
+            cfg=cfg,
+            rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage2_out['proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 Fast R-CNN, init from stage 2 RPN R-CNN model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=str(rpn_stage2_out['model_path']),
+            solver=solvers[3],
+            max_iters=max_iters[3],
+            cfg=cfg,
+            rpn_file=rpn_stage2_out['proposal_path'])
+    p = mp.Process(target=train_fast_rcnn, kwargs=mp_kwargs)
+    p.start()
+    fast_rcnn_stage2_out = mp_queue.get()
+    p.join()
+
+    # Create final model (just a copy of the last stage)
+    final_path = os.path.join(
+            os.path.dirname(fast_rcnn_stage2_out['model_path']),
+            args.net_name + '_faster_rcnn_final.caffemodel')
+    print 'cp {} -> {}'.format(
+            fast_rcnn_stage2_out['model_path'], final_path)
+    shutil.copy(fast_rcnn_stage2_out['model_path'], final_path)
+    print 'Final model: {}'.format(final_path)
diff --git a/examples/rfcn/tools/train_net.py b/examples/rfcn/tools/train_net.py
new file mode 100755
index 000000000..3c6e6fbb6
--- /dev/null
+++ b/examples/rfcn/tools/train_net.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Train a Fast R-CNN network on a region of interest database."""
+
+import _init_paths
+from fast_rcnn.train import get_training_roidb, train_net
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
+from datasets.factory import get_imdb
+import datasets.imdb
+import caffe
+import argparse
+import pprint
+import numpy as np
+import sys
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
+    parser.add_argument('--gpu', dest='gpu_id',
+                        help='GPU device id to use [0]',
+                        default=0, type=int)
+    parser.add_argument('--solver', dest='solver',
+                        help='solver prototxt',
+                        default=None, type=str)
+    parser.add_argument('--iters', dest='max_iters',
+                        help='number of iterations to train',
+                        default=40000, type=int)
+    parser.add_argument('--weights', dest='pretrained_model',
+                        help='initialize with pretrained model weights',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file',
+                        default=None, type=str)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to train on',
+                        default='voc_2007_trainval', type=str)
+    parser.add_argument('--rand', dest='randomize',
+                        help='randomize (do not use a fixed seed)',
+                        action='store_true')
+    parser.add_argument('--set', dest='set_cfgs',
+                        help='set config keys', default=None,
+                        nargs=argparse.REMAINDER)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+def combined_roidb(imdb_names):
+    def get_roidb(imdb_name):
+        imdb = get_imdb(imdb_name)
+        print 'Loaded dataset `{:s}` for training'.format(imdb.name)
+        imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
+        print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
+        roidb = get_training_roidb(imdb)
+        return roidb
+
+    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
+    roidb = roidbs[0]
+    if len(roidbs) > 1:
+        for r in roidbs[1:]:
+            roidb.extend(r)
+        imdb = datasets.imdb.imdb(imdb_names)
+    else:
+        imdb = get_imdb(imdb_names)
+    return imdb, roidb
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+
+    cfg.GPU_ID = args.gpu_id
+
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    if not args.randomize:
+        # fix the random seeds (numpy and caffe) for reproducibility
+        np.random.seed(cfg.RNG_SEED)
+        caffe.set_random_seed(cfg.RNG_SEED)
+
+    # set up caffe
+    #caffe.set_mode_gpu()
+    #caffe.set_device(args.gpu_id)
+
+    imdb, roidb = combined_roidb(args.imdb_name)
+    print '{:d} roidb entries'.format(len(roidb))
+
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+
+    train_net(args.solver, roidb, output_dir,
+              pretrained_model=args.pretrained_model,
+              max_iters=args.max_iters)
diff --git a/examples/rfcn/tools/train_rfcn_alt_opt_5stage.py b/examples/rfcn/tools/train_rfcn_alt_opt_5stage.py
new file mode 100755
index 000000000..bc2ec655c
--- /dev/null
+++ b/examples/rfcn/tools/train_rfcn_alt_opt_5stage.py
@@ -0,0 +1,477 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# R-FCN
+# Copyright (c) 2016 Yuwen Xiong, Haozhi Qi
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+"""Train a R-FCN network using alternating optimization.
+This tool implements the alternating optimization algorithm described in our
+NIPS 2015 paper ("R-FCN: Towards Real-time Object Detection with Region
+Proposal Networks." Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.)
+"""
+
+import _init_paths
+from fast_rcnn.train import get_training_roidb, train_net
+from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
+from datasets.factory import get_imdb
+from rpn.generate import imdb_proposals, imdb_rpn_compute_stats
+import argparse
+import pprint
+import numpy as np
+import sys, os
+import multiprocessing as mp
+import cPickle
+import shutil
+
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Train a R-FCN network')
+    parser.add_argument('--gpu', dest='gpu_id',
+                        help='GPU device id to use [0]',
+                        default=0, type=int)
+    parser.add_argument('--net_name', dest='net_name',
+                        help='network name (e.g., "ResNet-101")',
+                        default=None, type=str)
+    parser.add_argument('--weights', dest='pretrained_model',
+                        help='initialize with pretrained model weights',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file',
+                        default=None, type=str)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to train on',
+                        default='voc_2007_trainval', type=str)
+    parser.add_argument('--imdb_test', dest='imdb_test_name',
+                        help='dataset to test',
+                        default='voc_2007_test', type=str)
+    parser.add_argument('--set', dest='set_cfgs',
+                        help='set config keys', default=None,
+                        nargs=argparse.REMAINDER)
+    parser.add_argument('--model', dest='model_name',
+                        help='folder name of model',
+                        default=None, type=str)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+
+def get_roidb(imdb_name, rpn_file=None):
+    imdb = get_imdb(imdb_name)
+    print 'Loaded dataset `{:s}` for training'.format(imdb.name)
+    imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
+    print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
+    if rpn_file is not None:
+        imdb.config['rpn_file'] = rpn_file
+    roidb = get_training_roidb(imdb)
+    return roidb, imdb
+
+
+def get_solvers(imdb_name, net_name, model_name):
+    # R-FCN Alternating Optimization
+    # Solver for each training stage
+    if imdb_name.startswith('coco'):
+        solvers = [[net_name, model_name, 'stage1_rpn_solver360k480k.pt'],
+                   [net_name, model_name, 'stage1_rfcn_ohem_solver360k480k.pt'],
+                   [net_name, model_name, 'stage2_rpn_solver360k480k.pt'],
+                   [net_name, model_name, 'stage2_rfcn_ohem_solver360k480k.pt'],
+                   [net_name, model_name, 'stage3_rpn_solver360k480k.pt']]
+        solvers = [os.path.join('.', 'models', 'coco', *s) for s in solvers]
+        # Iterations for each training stage
+        max_iters = [480000, 480000, 480000, 480000, 480000]
+        # Test prototxt for the RPN
+        rpn_test_prototxt = os.path.join(
+            '.', 'models', 'coco', net_name, model_name, 'rpn_test.pt')
+    else:
+        solvers = [[net_name, model_name, 'stage1_rpn_solver60k80k.pt'],
+                   [net_name, model_name, 'stage1_rfcn_ohem_solver80k120k.pt'],
+                   [net_name, model_name, 'stage2_rpn_solver60k80k.pt'],
+                   [net_name, model_name, 'stage2_rfcn_ohem_solver80k120k.pt'],
+                   [net_name, model_name, 'stage3_rpn_solver60k80k.pt']]
+        solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
+        # Iterations for each training stage
+        max_iters = [80000, 120000, 80000, 120000, 80000]
+        # Test prototxt for the RPN
+        rpn_test_prototxt = os.path.join(
+            cfg.MODELS_DIR, net_name, model_name, 'rpn_test.pt')
+    return solvers, max_iters, rpn_test_prototxt
+
+
+def _init_caffe(cfg):
+    """Initialize pycaffe in a training process.
+    """
+
+    import caffe
+    # fix the random seeds (numpy and caffe) for reproducibility
+    np.random.seed(cfg.RNG_SEED)
+    caffe.set_random_seed(cfg.RNG_SEED)
+    # set up caffe
+    caffe.set_mode_gpu()
+    caffe.set_device(cfg.GPU_ID)
+
+
+def train_rpn(queue=None, imdb_name=None, init_model=None, solver=None,
+              max_iters=None, cfg=None, output_cache=None):
+    """Train a Region Proposal Network in a separate training process.
+    """
+
+    # Not using any proposals, just ground-truth boxes
+    cfg.TRAIN.HAS_RPN = True
+    cfg.TRAIN.BBOX_REG = False  # applies only to R-FCN bbox regression
+    cfg.TRAIN.PROPOSAL_METHOD = 'gt'
+    cfg.TRAIN.IMS_PER_BATCH = 1
+    print 'Init model: {}'.format(init_model)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    roidb, imdb = get_roidb(imdb_name)
+    print 'roidb len: {}'.format(len(roidb))
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+    final_caffemodel = os.path.join(output_dir, output_cache)
+
+    if os.path.exists(final_caffemodel):
+        queue.put({'model_path': final_caffemodel})
+    else:
+        model_paths = train_net(solver, roidb, output_dir,
+                                pretrained_model=init_model,
+                                max_iters=max_iters)
+        # Cleanup all but the final model
+        for i in model_paths[:-1]:
+            os.remove(i)
+        rpn_model_path = model_paths[-1]
+        # Send final model path through the multiprocessing queue
+        shutil.copyfile(rpn_model_path, final_caffemodel)
+        queue.put({'model_path': final_caffemodel})
+
+
+def rpn_generate(queue=None, imdb_name=None, rpn_model_path=None, cfg=None,
+                 rpn_test_prototxt=None):
+    """Use a trained RPN to generate proposals.
+    """
+
+    cfg.TEST.RPN_PRE_NMS_TOP_N = 6000     # no pre NMS filtering
+    cfg.TEST.RPN_POST_NMS_TOP_N = 300  # limit top boxes after NMS
+    print 'RPN model: {}'.format(rpn_model_path)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    # NOTE: the matlab implementation computes proposals on flipped images, too.
+    # We compute them on the image once and then flip the already computed
+    # proposals. This might cause a minor loss in mAP (less proposal jittering).
+    imdb = get_imdb(imdb_name)
+    print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name)
+
+    # Load RPN and configure output directory
+    rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST)
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+    rpn_net_name = os.path.splitext(os.path.basename(rpn_model_path))[0]
+    rpn_proposals_path = os.path.join(
+        output_dir, rpn_net_name + '_proposals.pkl')
+
+    # Generate proposals on the imdb
+
+    # Write proposals to disk and send the proposal file path through the
+    # multiprocessing queue
+    if not os.path.exists(rpn_proposals_path):
+        rpn_proposals = imdb_proposals(rpn_net, imdb)
+        with open(rpn_proposals_path, 'wb') as f:
+            cPickle.dump(rpn_proposals, f, cPickle.HIGHEST_PROTOCOL)
+    queue.put({'proposal_path': rpn_proposals_path})
+    print 'Wrote RPN proposals to {}'.format(rpn_proposals_path)
+
+
+def train_rfcn(queue=None, imdb_name=None, init_model=None, solver=None,
+                    max_iters=None, cfg=None, rpn_file=None, output_cache=None):
+    """Train a R-FCN using proposals generated by an RPN.
+    """
+
+    cfg.TRAIN.HAS_RPN = False           # not generating prosals on-the-fly
+    cfg.TRAIN.PROPOSAL_METHOD = 'rpn'   # use pre-computed RPN proposals instead
+    cfg.TRAIN.IMS_PER_BATCH = 1
+    print 'Init model: {}'.format(init_model)
+    print 'RPN proposals: {}'.format(rpn_file)
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    import caffe
+    _init_caffe(cfg)
+
+    roidb, imdb = get_roidb(imdb_name, rpn_file=rpn_file)
+    output_dir = get_output_dir(imdb)
+    print 'Output will be saved to `{:s}`'.format(output_dir)
+    # Train R-FCN
+    # Send R-FCN model path over the multiprocessing queue
+    final_caffemodel = os.path.join(output_dir, output_cache)
+
+    if os.path.exists(final_caffemodel):
+        queue.put({'model_path': final_caffemodel})
+    else:
+        model_paths = train_net(solver, roidb, output_dir,
+                                pretrained_model=init_model,
+                                max_iters=max_iters)
+        # Cleanup all but the final model
+        for i in model_paths[:-1]:
+            os.remove(i)
+        rfcn_model_path = model_paths[-1]
+        # Send final model path through the multiprocessing queue
+        shutil.copyfile(rfcn_model_path, final_caffemodel)
+        queue.put({'model_path': final_caffemodel})
+
+
+def rpn_compute_stats(queue=None, imdb_name=None, cfg=None, rpn_test_prototxt=None):
+    """Compute mean stds for anchors
+    """
+    cfg.TRAIN.HAS_RPN = True
+    cfg.TRAIN.BBOX_REG = False  # applies only to R-FCN bbox regression
+    cfg.TRAIN.PROPOSAL_METHOD = 'gt'
+    cfg.TRAIN.IMS_PER_BATCH = 1
+
+    import caffe
+    _init_caffe(cfg)
+
+    # NOTE: the matlab implementation computes proposals on flipped images, too.
+    # We compute them on the image once and then flip the already computed
+    # proposals. This might cause a minor loss in mAP (less proposal jittering).
+    roidb, imdb = get_roidb(imdb_name)
+    print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name)
+    mean_file = os.path.join(imdb.cache_path, imdb.name + '_means.npy')
+    std_file = os.path.join(imdb.cache_path, imdb.name + '_stds.npy')
+    if os.path.exists(mean_file) and os.path.exists(std_file):
+        means = np.load(mean_file)
+        stds = np.load(std_file)
+    else:
+        # Load RPN and configure output directory
+        rpn_net = caffe.Net(rpn_test_prototxt, caffe.TEST)
+        # Generate proposals on the imdb
+        print 'start computing means/stds, it may take several minutes...'
+        if imdb_name.startswith('coco'):
+            means, stds = imdb_rpn_compute_stats(rpn_net, imdb, anchor_scales=(4, 8, 16, 32))
+        else:
+            means, stds = imdb_rpn_compute_stats(rpn_net, imdb, anchor_scales=(8, 16, 32))
+        np.save(mean_file, means)
+        np.save(std_file, stds)
+    queue.put({'means': means, 'stds': stds})
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+    cfg.GPU_ID = args.gpu_id
+
+    # --------------------------------------------------------------------------
+    # Pycaffe doesn't reliably free GPU memory when instantiated nets are
+    # discarded (e.g. "del net" in Python code). To work around this issue, each
+    # training stage is executed in a separate process using
+    # multiprocessing.Process.
+    # --------------------------------------------------------------------------
+
+    # queue for communicated results between processes
+    mp_queue = mp.Queue()
+    # solves, iters, etc. for each training stage
+    solvers, max_iters, rpn_test_prototxt = get_solvers(args.imdb_name, args.net_name, args.model_name)
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 0 RPN, compute normalization means and stds'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_name,
+        cfg=cfg,
+        rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_compute_stats, kwargs=mp_kwargs)
+    p.start()
+    stage0_anchor_stats = mp_queue.get()
+    p.join()
+    cfg.TRAIN.RPN_NORMALIZE_MEANS = stage0_anchor_stats['means']
+    cfg.TRAIN.RPN_NORMALIZE_STDS = stage0_anchor_stats['stds']
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 RPN, init from ImageNet model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=args.pretrained_model,
+            solver=solvers[0],
+            max_iters=max_iters[0],
+            cfg=cfg,
+            output_cache='stage1_rpn_final.caffemodel')
+    p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage1_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 RPN, generate proposals'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            rpn_model_path=str(rpn_stage1_out['model_path']),
+            cfg=cfg,
+            rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage1_out['proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 RPN, generate proposals'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_test_name,
+        rpn_model_path=str(rpn_stage1_out['model_path']),
+        cfg=cfg,
+        rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage1_out['test_proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 1 R-FCN using RPN proposals, init from ImageNet model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
+    mp_kwargs = dict(
+            queue=mp_queue,
+            imdb_name=args.imdb_name,
+            init_model=args.pretrained_model,
+            solver=solvers[1],
+            max_iters=max_iters[1],
+            cfg=cfg,
+            rpn_file=rpn_stage1_out['proposal_path'],
+            output_cache='stage1_rfcn_final.caffemodel')
+    p = mp.Process(target=train_rfcn, kwargs=mp_kwargs)
+    p.start()
+    rfcn_stage1_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 RPN, init from stage1 R-FCN model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_name,
+        init_model=str(rfcn_stage1_out['model_path']),
+        solver=solvers[2],
+        max_iters=max_iters[2],
+        cfg=cfg,
+        output_cache='stage2_rpn_final.caffemodel')
+    p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage2_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 RPN, generate proposals'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_name,
+        rpn_model_path=str(rpn_stage2_out['model_path']),
+        cfg=cfg,
+        rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage2_out['proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_test_name,
+        rpn_model_path=str(rpn_stage2_out['model_path']),
+        cfg=cfg,
+        rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage2_out['test_proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 2 R-FCN using Stage-2 RPN proposals, init from ImageNet model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_name,
+        init_model=args.pretrained_model,
+        solver=solvers[3],
+        max_iters=max_iters[3],
+        cfg=cfg,
+        rpn_file=rpn_stage2_out['proposal_path'],
+        output_cache='stage2_rfcn_final.caffemodel')
+    p = mp.Process(target=train_rfcn, kwargs=mp_kwargs)
+    p.start()
+    rfcn_stage2_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 3 RPN, init from stage1 R-FCN model'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    cfg.TRAIN.SNAPSHOT_INFIX = 'stage3'
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_name,
+        init_model=str(rfcn_stage2_out['model_path']),
+        solver=solvers[4],
+        max_iters=max_iters[4],
+        cfg=cfg,
+        output_cache='stage3_rpn_final.caffemodel')
+    p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage3_out = mp_queue.get()
+    p.join()
+
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+    print 'Stage 3 RPN, generate test proposals only'
+    print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
+
+    mp_kwargs = dict(
+        queue=mp_queue,
+        imdb_name=args.imdb_test_name,
+        rpn_model_path=str(rpn_stage3_out['model_path']),
+        cfg=cfg,
+        rpn_test_prototxt=rpn_test_prototxt)
+    p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
+    p.start()
+    rpn_stage3_out['test_proposal_path'] = mp_queue.get()['proposal_path']
+    p.join()
+
+    print 'Final model: {}'.format(str(rfcn_stage2_out['model_path']))
+    print 'Final RPN: {}'.format(str(rpn_stage3_out['test_proposal_path']))
+
+
diff --git a/examples/rfcn/tools/train_svms.py b/examples/rfcn/tools/train_svms.py
new file mode 100755
index 000000000..498bbf2a2
--- /dev/null
+++ b/examples/rfcn/tools/train_svms.py
@@ -0,0 +1,353 @@
+#!/usr/bin/env python
+
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""
+Train post-hoc SVMs using the algorithm and hyper-parameters from
+traditional R-CNN.
+"""
+
+import _init_paths
+from fast_rcnn.config import cfg, cfg_from_file
+from datasets.factory import get_imdb
+from fast_rcnn.test import im_detect
+from utils.timer import Timer
+import caffe
+import argparse
+import pprint
+import numpy as np
+import numpy.random as npr
+import cv2
+from sklearn import svm
+import os, sys
+
+class SVMTrainer(object):
+    """
+    Trains post-hoc detection SVMs for all classes using the algorithm
+    and hyper-parameters of traditional R-CNN.
+    """
+
+    def __init__(self, net, imdb):
+        self.imdb = imdb
+        self.net = net
+        self.layer = 'fc7'
+        self.hard_thresh = -1.0001
+        self.neg_iou_thresh = 0.3
+
+        dim = net.params['cls_score'][0].data.shape[1]
+        scale = self._get_feature_scale()
+        print('Feature dim: {}'.format(dim))
+        print('Feature scale: {:.3f}'.format(scale))
+        self.trainers = [SVMClassTrainer(cls, dim, feature_scale=scale)
+                         for cls in imdb.classes]
+
+    def _get_feature_scale(self, num_images=100):
+        TARGET_NORM = 20.0 # Magic value from traditional R-CNN
+        _t = Timer()
+        roidb = self.imdb.roidb
+        total_norm = 0.0
+        count = 0.0
+        inds = npr.choice(xrange(self.imdb.num_images), size=num_images,
+                          replace=False)
+        for i_, i in enumerate(inds):
+            im = cv2.imread(self.imdb.image_path_at(i))
+            if roidb[i]['flipped']:
+                im = im[:, ::-1, :]
+            _t.tic()
+            scores, boxes = im_detect(self.net, im, roidb[i]['boxes'])
+            _t.toc()
+            feat = self.net.blobs[self.layer].data
+            total_norm += np.sqrt((feat ** 2).sum(axis=1)).sum()
+            count += feat.shape[0]
+            print('{}/{}: avg feature norm: {:.3f}'.format(i_ + 1, num_images,
+                                                           total_norm / count))
+
+        return TARGET_NORM * 1.0 / (total_norm / count)
+
+    def _get_pos_counts(self):
+        counts = np.zeros((len(self.imdb.classes)), dtype=np.int)
+        roidb = self.imdb.roidb
+        for i in xrange(len(roidb)):
+            for j in xrange(1, self.imdb.num_classes):
+                I = np.where(roidb[i]['gt_classes'] == j)[0]
+                counts[j] += len(I)
+
+        for j in xrange(1, self.imdb.num_classes):
+            print('class {:s} has {:d} positives'.
+                  format(self.imdb.classes[j], counts[j]))
+
+        return counts
+
+    def get_pos_examples(self):
+        counts = self._get_pos_counts()
+        for i in xrange(len(counts)):
+            self.trainers[i].alloc_pos(counts[i])
+
+        _t = Timer()
+        roidb = self.imdb.roidb
+        num_images = len(roidb)
+        # num_images = 100
+        for i in xrange(num_images):
+            im = cv2.imread(self.imdb.image_path_at(i))
+            if roidb[i]['flipped']:
+                im = im[:, ::-1, :]
+            gt_inds = np.where(roidb[i]['gt_classes'] > 0)[0]
+            gt_boxes = roidb[i]['boxes'][gt_inds]
+            _t.tic()
+            scores, boxes = im_detect(self.net, im, gt_boxes)
+            _t.toc()
+            feat = self.net.blobs[self.layer].data
+            for j in xrange(1, self.imdb.num_classes):
+                cls_inds = np.where(roidb[i]['gt_classes'][gt_inds] == j)[0]
+                if len(cls_inds) > 0:
+                    cls_feat = feat[cls_inds, :]
+                    self.trainers[j].append_pos(cls_feat)
+
+            print 'get_pos_examples: {:d}/{:d} {:.3f}s' \
+                  .format(i + 1, len(roidb), _t.average_time)
+
+    def initialize_net(self):
+        # Start all SVM parameters at zero
+        self.net.params['cls_score'][0].data[...] = 0
+        self.net.params['cls_score'][1].data[...] = 0
+
+        # Initialize SVMs in a smart way. Not doing this because its such
+        # a good initialization that we might not learn something close to
+        # the SVM solution.
+#        # subtract background weights and biases for the foreground classes
+#        w_bg = self.net.params['cls_score'][0].data[0, :]
+#        b_bg = self.net.params['cls_score'][1].data[0]
+#        self.net.params['cls_score'][0].data[1:, :] -= w_bg
+#        self.net.params['cls_score'][1].data[1:] -= b_bg
+#        # set the background weights and biases to 0 (where they shall remain)
+#        self.net.params['cls_score'][0].data[0, :] = 0
+#        self.net.params['cls_score'][1].data[0] = 0
+
+    def update_net(self, cls_ind, w, b):
+        self.net.params['cls_score'][0].data[cls_ind, :] = w
+        self.net.params['cls_score'][1].data[cls_ind] = b
+
+    def train_with_hard_negatives(self):
+        _t = Timer()
+        roidb = self.imdb.roidb
+        num_images = len(roidb)
+        # num_images = 100
+        for i in xrange(num_images):
+            im = cv2.imread(self.imdb.image_path_at(i))
+            if roidb[i]['flipped']:
+                im = im[:, ::-1, :]
+            _t.tic()
+            scores, boxes = im_detect(self.net, im, roidb[i]['boxes'])
+            _t.toc()
+            feat = self.net.blobs[self.layer].data
+            for j in xrange(1, self.imdb.num_classes):
+                hard_inds = \
+                    np.where((scores[:, j] > self.hard_thresh) &
+                             (roidb[i]['gt_overlaps'][:, j].toarray().ravel() <
+                              self.neg_iou_thresh))[0]
+                if len(hard_inds) > 0:
+                    hard_feat = feat[hard_inds, :].copy()
+                    new_w_b = \
+                        self.trainers[j].append_neg_and_retrain(feat=hard_feat)
+                    if new_w_b is not None:
+                        self.update_net(j, new_w_b[0], new_w_b[1])
+
+            print(('train_with_hard_negatives: '
+                   '{:d}/{:d} {:.3f}s').format(i + 1, len(roidb),
+                                               _t.average_time))
+
+    def train(self):
+        # Initialize SVMs using
+        #   a. w_i = fc8_w_i - fc8_w_0
+        #   b. b_i = fc8_b_i - fc8_b_0
+        #   c. Install SVMs into net
+        self.initialize_net()
+
+        # Pass over roidb to count num positives for each class
+        #   a. Pre-allocate arrays for positive feature vectors
+        # Pass over roidb, computing features for positives only
+        self.get_pos_examples()
+
+        # Pass over roidb
+        #   a. Compute cls_score with forward pass
+        #   b. For each class
+        #       i. Select hard negatives
+        #       ii. Add them to cache
+        #   c. For each class
+        #       i. If SVM retrain criteria met, update SVM
+        #       ii. Install new SVM into net
+        self.train_with_hard_negatives()
+
+        # One final SVM retraining for each class
+        # Install SVMs into net
+        for j in xrange(1, self.imdb.num_classes):
+            new_w_b = self.trainers[j].append_neg_and_retrain(force=True)
+            self.update_net(j, new_w_b[0], new_w_b[1])
+
+class SVMClassTrainer(object):
+    """Manages post-hoc SVM training for a single object class."""
+
+    def __init__(self, cls, dim, feature_scale=1.0,
+                 C=0.001, B=10.0, pos_weight=2.0):
+        self.pos = np.zeros((0, dim), dtype=np.float32)
+        self.neg = np.zeros((0, dim), dtype=np.float32)
+        self.B = B
+        self.C = C
+        self.cls = cls
+        self.pos_weight = pos_weight
+        self.dim = dim
+        self.feature_scale = feature_scale
+        self.svm = svm.LinearSVC(C=C, class_weight={1: 2, -1: 1},
+                                 intercept_scaling=B, verbose=1,
+                                 penalty='l2', loss='l1',
+                                 random_state=cfg.RNG_SEED, dual=True)
+        self.pos_cur = 0
+        self.num_neg_added = 0
+        self.retrain_limit = 2000
+        self.evict_thresh = -1.1
+        self.loss_history = []
+
+    def alloc_pos(self, count):
+        self.pos_cur = 0
+        self.pos = np.zeros((count, self.dim), dtype=np.float32)
+
+    def append_pos(self, feat):
+        num = feat.shape[0]
+        self.pos[self.pos_cur:self.pos_cur + num, :] = feat
+        self.pos_cur += num
+
+    def train(self):
+        print('>>> Updating {} detector <<<'.format(self.cls))
+        num_pos = self.pos.shape[0]
+        num_neg = self.neg.shape[0]
+        print('Cache holds {} pos examples and {} neg examples'.
+              format(num_pos, num_neg))
+        X = np.vstack((self.pos, self.neg)) * self.feature_scale
+        y = np.hstack((np.ones(num_pos),
+                       -np.ones(num_neg)))
+        self.svm.fit(X, y)
+        w = self.svm.coef_
+        b = self.svm.intercept_[0]
+        scores = self.svm.decision_function(X)
+        pos_scores = scores[:num_pos]
+        neg_scores = scores[num_pos:]
+
+        pos_loss = (self.C * self.pos_weight *
+                    np.maximum(0, 1 - pos_scores).sum())
+        neg_loss = self.C * np.maximum(0, 1 + neg_scores).sum()
+        reg_loss = 0.5 * np.dot(w.ravel(), w.ravel()) + 0.5 * b ** 2
+        tot_loss = pos_loss + neg_loss + reg_loss
+        self.loss_history.append((tot_loss, pos_loss, neg_loss, reg_loss))
+
+        for i, losses in enumerate(self.loss_history):
+            print(('    {:d}: obj val: {:.3f} = {:.3f} '
+                   '(pos) + {:.3f} (neg) + {:.3f} (reg)').format(i, *losses))
+
+        # Sanity check
+        scores_ret = (
+                X * 1.0 / self.feature_scale).dot(w.T * self.feature_scale) + b
+        assert np.allclose(scores, scores_ret[:, 0], atol=1e-5), \
+                "Scores from returned model don't match decision function"
+
+        return ((w * self.feature_scale, b), pos_scores, neg_scores)
+
+    def append_neg_and_retrain(self, feat=None, force=False):
+        if feat is not None:
+            num = feat.shape[0]
+            self.neg = np.vstack((self.neg, feat))
+            self.num_neg_added += num
+        if self.num_neg_added > self.retrain_limit or force:
+            self.num_neg_added = 0
+            new_w_b, pos_scores, neg_scores = self.train()
+            # scores = np.dot(self.neg, new_w_b[0].T) + new_w_b[1]
+            # easy_inds = np.where(neg_scores < self.evict_thresh)[0]
+            not_easy_inds = np.where(neg_scores >= self.evict_thresh)[0]
+            if len(not_easy_inds) > 0:
+                self.neg = self.neg[not_easy_inds, :]
+                # self.neg = np.delete(self.neg, easy_inds)
+            print('    Pruning easy negatives')
+            print('    Cache holds {} pos examples and {} neg examples'.
+                  format(self.pos.shape[0], self.neg.shape[0]))
+            print('    {} pos support vectors'.format((pos_scores <= 1).sum()))
+            print('    {} neg support vectors'.format((neg_scores >= -1).sum()))
+            return new_w_b
+        else:
+            return None
+
+def parse_args():
+    """
+    Parse input arguments
+    """
+    parser = argparse.ArgumentParser(description='Train SVMs (old skool)')
+    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
+                        default=0, type=int)
+    parser.add_argument('--def', dest='prototxt',
+                        help='prototxt file defining the network',
+                        default=None, type=str)
+    parser.add_argument('--net', dest='caffemodel',
+                        help='model to test',
+                        default=None, type=str)
+    parser.add_argument('--cfg', dest='cfg_file',
+                        help='optional config file', default=None, type=str)
+    parser.add_argument('--imdb', dest='imdb_name',
+                        help='dataset to train on',
+                        default='voc_2007_trainval', type=str)
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    # Must turn this off to prevent issues when digging into the net blobs to
+    # pull out features (tricky!)
+    cfg.DEDUP_BOXES = 0
+
+    # Must turn this on because we use the test im_detect() method to harvest
+    # hard negatives
+    cfg.TEST.SVM = True
+
+    args = parse_args()
+
+    print('Called with args:')
+    print(args)
+
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+
+    print('Using config:')
+    pprint.pprint(cfg)
+
+    # fix the random seed for reproducibility
+    np.random.seed(cfg.RNG_SEED)
+
+    # set up caffe
+    caffe.set_mode_gpu()
+    if args.gpu_id is not None:
+        caffe.set_device(args.gpu_id)
+    net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
+    net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]
+    out = os.path.splitext(os.path.basename(args.caffemodel))[0] + '_svm'
+    out_dir = os.path.dirname(args.caffemodel)
+
+    imdb = get_imdb(args.imdb_name)
+    print 'Loaded dataset `{:s}` for training'.format(imdb.name)
+
+    # enhance roidb to contain flipped examples
+    if cfg.TRAIN.USE_FLIPPED:
+        print 'Appending horizontally-flipped training examples...'
+        imdb.append_flipped_images()
+        print 'done'
+
+    SVMTrainer(net, imdb).train()
+
+    filename = '{}/{}.caffemodel'.format(out_dir, out)
+    net.save(filename)
+    print 'Wrote svm model to: {:s}'.format(filename)
diff --git a/external/mkl/prepare_mkl.sh b/external/mkl/prepare_mkl.sh
index f69e79423..708a746c3 100755
--- a/external/mkl/prepare_mkl.sh
+++ b/external/mkl/prepare_mkl.sh
@@ -85,11 +85,12 @@ VERSION_LINE=`GetVersionName $MKLROOT`
 # Check if MKLROOT is set if positive then set one will be used..
 if [ -z $MKLROOT ] || [ $VERSION_LINE -lt $VERSION_MATCH ]; then
 	# ..if MKLROOT is not set then check if we have MKL downloaded in proper version
-    VERSION_LINE=`GetVersionName $DST/$MKL_CONTENT_DIR`
+    VERSION_LINE=`GetVersionName $DST/mklml`
     if [ $VERSION_LINE -lt $VERSION_MATCH ] ; then
       #...If it is not then downloaded and unpacked
       wget --no-check-certificate -P $DST $MKLURL -O $DST/$ARCHIVE_BASENAME
       tar -xzf $DST/$ARCHIVE_BASENAME -C $DST
+      ln -sf $MKL_CONTENT_DIR $DST/mklml
     fi
   FindLibrary $DST
   MKLROOT=$PWD/`echo $LOCALMKL | sed -e 's/lib.*$//'`
diff --git a/external/mlsl/prepare_mlsl.sh b/external/mlsl/prepare_mlsl.sh
index a998c393f..1fb0b3290 100755
--- a/external/mlsl/prepare_mlsl.sh
+++ b/external/mlsl/prepare_mlsl.sh
@@ -1,7 +1,7 @@
 #!/bin/sh
 # set -ex
 # 
-# All modification made by Intel Corporation: © 2016 Intel Corporation
+# All modification made by Intel Corporation: © 2018 Intel Corporation
 # 
 # This script is used to prepare the Intel® Machine Learning Scaling Library
 #
@@ -58,22 +58,26 @@ fi
 }
 
 # Clean up the previous MLSL version
-CleanUpPreviousMLSL2017_1_016()
+# Can be used for l_mlsl_2017.1.016, l_mlsl_2017.2.018
+function CleanUpPreviousMLSL()
 {
+version_year=$1
+version_num=$2
+subversion_num=$3
 OLD_ARCHIVE_TARGZ=files.tar.gz
-OLD_ARCHIVE_BASENAME=l_mlsl_2017.1.016.tgz
-OLD_ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_2017.1.016
+OLD_ARCHIVE_BASENAME=l_mlsl_$version_year.$version_num.$subversion_num.tgz
+OLD_ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_$version_year.$version_num.$subversion_num
 if [ -f $ABS_DST/$OLD_ARCHIVE_TARGZ ]; then
   rm $ABS_DST/$OLD_ARCHIVE_TARGZ
   #echo "[Debug] Delete old files.tar.gz!"
 fi
 if [ -f $ABS_DST/$OLD_ARCHIVE_BASENAME ]; then
   rm $ABS_DST/$OLD_ARCHIVE_BASENAME
-  #echo "[Debug] Delete old l_mlsl_2017.1.016.tgz file!"
+  #echo "[Debug] Delete old l_mlsl_$version_year.$version_num.$subversion_num.tgz file!"
 fi
 if [ -d $ABS_DST/$OLD_ARCHIVE_INSTALL_FOLDERNAME ]; then 
   rm -rf $ABS_DST/$OLD_ARCHIVE_INSTALL_FOLDERNAME
-  #echo "[Debug] Delete old l_mlsl_2017.1.016 folder!"
+  #echo "[Debug] Delete old l_mlsl_$version_year.$version_num.$subversion_num folder!"
 fi
 } 
 
@@ -85,19 +89,21 @@ ABS_DST=`readlink -f $DST`
 #echo "[Debug] ABS_DST value: $ABS_DST"
 
 if [ -z $MLSL_ROOT ]; then
+  #l_mlsl_p_2017.0.014 version has the different structure, so keep a seprate version
   CleanUpPreviousMLSL2017_0_014
-  CleanUpPreviousMLSL2017_1_016
+  CleanUpPreviousMLSL 2017 1 016
+  CleanUpPreviousMLSL 2017 2 018
 fi
 
-VERSION_MATCH=20172018
-ARCHIVE_BASENAME=l_mlsl_2017.2.018.tgz
-ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_2017.2.018
-#because the l_mlsl_2017.2.018.tgz will unpacked files.tar.gz and install.sh to the ARCHIVE_INSTALL_FOLDERNAME
+VERSION_MATCH=20180003
+ARCHIVE_BASENAME=l_mlsl_2018.0.003.tgz
+ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_2018.0.003
+#because the l_mlsl_2018.0.003.tgz will unpacked files.tar.gz and install.sh to the ARCHIVE_INSTALL_FOLDERNAME
 #not unpacked to the DST folder (Different behavior against l_mlsl_p_2017.0.014.tgz)
-ARCHIVE_INSTALL_FOLDERNAME_TEMP=l_mlsl_2017.2.018_temp
+ARCHIVE_INSTALL_FOLDERNAME_TEMP=l_mlsl_2018.0.003_temp
 MLSL_CONTENT_DIR=`echo $ARCHIVE_BASENAME | rev | cut -d "." -f 2- | rev`
 #echo "[Debug] MLSL_CONTENT_DIR value: $MLSL_CONTENT_DIR"
-GITHUB_RELEASE_TAG=v2017.2-Preview
+GITHUB_RELEASE_TAG=v2018-Preview
 
 MLSLURL="https://github.com/01org/MLSL/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME"
 #echo "[Debug] MLSL_ROOT value: $MLSL_ROOT"
@@ -126,14 +132,14 @@ if [ -z $MLSL_ROOT ] || [ $VERSION_LINE -lt $VERSION_MATCH ]; then
       wget --no-check-certificate -P $DST $MLSLURL -O $DST/$ARCHIVE_BASENAME
       if [ ! -d $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP ]; then
         mkdir $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP
-        #echo "[Debug] Create l_mlsl_2017.2.018_temp folder for unpacking!"
+        #echo "[Debug] Create l_mlsl_2018.0.003_temp folder for unpacking!"
       fi
       tar -xzf $DST/$ARCHIVE_BASENAME -C $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP
       #echo "[Debug] PWD value: $PWD"
       #install.sh did not support the relative path as the parameter
       bash $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP/$ARCHIVE_INSTALL_FOLDERNAME/install.sh -s -d $ABS_DST/$ARCHIVE_INSTALL_FOLDERNAME
       rm -rf $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP
-      #echo "[Debug] Remove l_mlsl_2017.2.018_temp folder for unpacking!"
+      #echo "[Debug] Remove l_mlsl_2018.0.003_temp folder for unpacking!"
     fi
     #else: version is just our expected version, no need to donload again, but need to set the MLSL_ROOT
     #do not change the value of MLSL_ROOT if MLSL_ROOT is set, but version is not given
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index b1ee3f25a..97846daa6 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -254,6 +254,11 @@ class Blob {
   }
 
   const Dtype* cpu_data() const;
+#ifdef CO_SIM
+  //These are APIs used for access data/diff without changing any CAFFE internal status.
+  Dtype* cpu_data_cosim() const;
+  Dtype* cpu_diff_cosim() const;
+#endif
   void set_cpu_data(Dtype* data);
   void set_cpu_diff(Dtype* diff);
 
diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp
index a4daa3cfd..c4a7551ea 100644
--- a/include/caffe/data_reader.hpp
+++ b/include/caffe/data_reader.hpp
@@ -86,6 +86,7 @@ class DataReader {
   class DBWrapper  {
    public:
     explicit DBWrapper(const LayerParameter& param);
+    virtual ~DBWrapper();
     virtual string value() = 0;
     virtual void Next() = 0;
    protected:
diff --git a/include/caffe/engine_parser.hpp b/include/caffe/engine_parser.hpp
index 1fa716e9b..7ab2302ef 100644
--- a/include/caffe/engine_parser.hpp
+++ b/include/caffe/engine_parser.hpp
@@ -85,6 +85,8 @@ class EngineParser {
 #endif
 
     LOG(FATAL) << "EngineParser: Unknown subengine: " << engineName;
+    // should never be here. it's used to eliminate a build warning #1011: missing return statement at end of non-void function.
+    return CpuEngine::Instance().get_engine(); 
   }
 #endif
 
diff --git a/include/caffe/mkl_memory.hpp b/include/caffe/mkl_memory.hpp
index 3d9de8373..2accebc38 100644
--- a/include/caffe/mkl_memory.hpp
+++ b/include/caffe/mkl_memory.hpp
@@ -138,6 +138,10 @@ struct MKLMemoryDescriptorBase : PrvMemDescr,
   }
   virtual void convert_from_prv(void* cpu_ptr);
   virtual void convert_to_prv(void* cpu_ptr);
+#ifdef CO_SIM
+    virtual void create_reorder_from_prv_cosim(void* cpu_ptr);
+    virtual void convert_from_prv_cosim(void* cpu_ptr);
+#endif
   virtual bool layout_compare(shared_ptr<PrvMemDescr> other);
   virtual void convert_from_other(shared_ptr<PrvMemDescr> other);
  protected:
diff --git a/include/caffe/mkldnn_memory.hpp b/include/caffe/mkldnn_memory.hpp
index 850028ff5..7bb4a3ae2 100644
--- a/include/caffe/mkldnn_memory.hpp
+++ b/include/caffe/mkldnn_memory.hpp
@@ -212,6 +212,14 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr
     shared_ptr<memory> _usr_memory;
     void* _cpu_ptr;
 
+#ifdef CO_SIM
+    shared_ptr<memory> _usr_memory_cosim;
+    MKLDNNPrimitive<Dtype> _reorder_prv2usr_cosim;
+    //Used to save the copy of the private memory
+    shared_ptr<memory> _prv_memory_cosim;
+    //Wrapper for readonly_prv_memory
+    shared_ptr<primitive::at>  at_prv_cosim;
+#endif
     MKLDNNLayer<Dtype>* _mkldnn_layer;
     Blob<Dtype>* _blob;
     std::vector<float> _scale = std::vector<float>(1,1.);
@@ -243,6 +251,11 @@ class MKLDNNMemoryDescriptor : public MKLDNNMemoryDescriptorBase<Dtype> {
     virtual void convert_to_prv(void* cpu_ptr);
     virtual void convert_from_extprv(shared_ptr<primitive> aprimitive);
     virtual bool on_to_cpu();
+#ifdef CO_SIM
+
+    virtual void create_reorder_from_prv_cosim(void* cpu_ptr);
+    virtual void convert_from_prv_cosim(void* cpu_ptr);
+#endif
 
     virtual void create_reorder_from_prv(void* cpu_ptr);
     virtual void create_reorder_to_prv(void* cpu_ptr);
diff --git a/include/caffe/multinode/multi_solver.hpp b/include/caffe/multinode/multi_solver.hpp
index bebab3c6c..c7de0d0cc 100644
--- a/include/caffe/multinode/multi_solver.hpp
+++ b/include/caffe/multinode/multi_solver.hpp
@@ -68,12 +68,12 @@ class MultiSolver {
 #endif
   }
 
-
   // Invoked at specific points during an iteration
   class Callback : public Solver<Dtype>::Callback {
   protected:
     virtual ~Callback() {
     }
+
     virtual void on_backward_finished(int layer_id) = 0;
     virtual void on_delwt_wait(int layer_id) = 0;
     virtual void apply_updates(int layer_id) = 0;
@@ -104,17 +104,33 @@ class MultiSolver {
   boost::shared_ptr<Solver<Dtype>> root_solver() {
     return root_solver_;
   }
+
+  void Backward(bool last=true);
+  Dtype Forward();
+  void WaitAndUpdate();
+  void ClearParamDiffs();
+
 #ifdef FW_OVERLAP_OPT
   void set_layer_finished_flag(int layer_id, bool flag) {
     layer_finished_flags_[layer_id] = flag;
   }
+
+  Dtype UpdateAndForward(bool first=true);
 #endif
+
  private:
   virtual Dtype ForwardBackwardImpl(bool first, bool last);
   bool IsSkipSyncGradient(int layer_id);
   bool WaitGradient(int layer_id);
   void UpdateGradient(int layer_id);
 
+  Dtype ForwardFromTo(int start, int end);
+  void BackwardFromTo(int start, int end, bool last=true);
+
+#ifdef FW_OVERLAP_OPT
+  void WaitAndUpdateBeforeFwd(int layer_id);
+#endif
+
  protected:
   boost::shared_ptr<Solver<Dtype>> root_solver_;
   int iter_size;
diff --git a/include/caffe/multinode/multi_sync.hpp b/include/caffe/multinode/multi_sync.hpp
index 969cb53ac..79d88b328 100644
--- a/include/caffe/multinode/multi_sync.hpp
+++ b/include/caffe/multinode/multi_sync.hpp
@@ -129,6 +129,9 @@ namespace caffe {
 
     virtual ~MultiSync() {
     }
+    boost::shared_ptr<MultiSolver<Dtype>> get_solver() {
+      return solver;
+    }
 
     void synchronize_parameters() {
       LOG(INFO) << "synchronize_params: bcast";
@@ -187,7 +190,7 @@ namespace caffe {
       }
     }
 
-    void run() {
+    void init() {
       LOG(WARNING) << "RUN: "
                    << "PER LAYER TIMINGS ARE"
 #ifdef CAFFE_PER_LAYER_TIMINGS
@@ -223,6 +226,11 @@ namespace caffe {
 #endif
 
       solver->add_callback(this);
+    }
+
+    void run() {
+      init();
+
       solver->Solve();
 
 #ifdef PERFORMANCE_MONITORING
@@ -231,6 +239,9 @@ namespace caffe {
     }
 
     void apply_updates(int layer_id) {
+      if (mn::use_param_server())
+        return;
+
       std::vector<int> &param_ids = layer_param_ids[layer_id];
       for (int i = 0; i < param_ids.size(); ++i) {
         solver->root_solver()->ApplyUpdate(param_ids[i]);
@@ -266,25 +277,44 @@ namespace caffe {
       }
     }
 
-    void launch_reduce(int layer_id, int param_id) {
+    void launch_reduce(int layer_id) {
       mn::Distribution& distrib = layers[layer_id]->GetDistribution();
-      Dtype* send_buff = NULL;
-      Dtype* recv_buff = NULL;
-      size_t buf_size = net_params[param_id]->count();
-      if (CAN_USE_PRV_DIFF(net_params[param_id])) {
-        send_buff = (Dtype*)net_params[param_id]->prv_diff();
-        recv_buff = net_params[param_id]->mutable_prv_diff();
-      }
-      else {
-        send_buff = (Dtype*)net_params[param_id]->cpu_diff();
-        recv_buff = net_params[param_id]->mutable_cpu_diff();
-      }
-      reduce_req_vec[param_id] =
-        distrib.reduce_async<Dtype,MLSL::ReductionType::RT_SUM,MLSL::GroupType::GT_DATA>(
-          send_buff, recv_buff, buf_size);
-      if (reduce_req_vec[param_id] != NULL && distrib.is_root(MLSL::GroupType::GT_DATA)) {
-        AsyncTask req_task(layer_id, param_id, NULL);
-        reduce_req_list.push_back(req_task);
+      boost::shared_ptr<Layer<Dtype>> &layer = layers[layer_id];
+      std::vector<int> &param_ids = layer_param_ids[layer_id];
+      // TODO: descending is faster?
+      for (int i = param_ids.size() - 1; i >= 0; --i) {
+        if (!layer->ParamNeedReduce(i)) continue;
+        int param_id = param_ids[i];
+
+        if (mn::get_nodes_count() > 1) {
+          Dtype* send_buff = NULL;
+          Dtype* recv_buff = NULL;
+          size_t buf_size = net_params[param_id]->count();
+          if (CAN_USE_PRV_DIFF(net_params[param_id])) {
+            send_buff = (Dtype*)net_params[param_id]->prv_diff();
+            recv_buff = net_params[param_id]->mutable_prv_diff();
+          }
+          else {
+            send_buff = (Dtype*)net_params[param_id]->cpu_diff();
+            recv_buff = net_params[param_id]->mutable_cpu_diff();
+          }
+          reduce_req_vec[param_id] =
+            distrib.reduce_async<Dtype,MLSL::ReductionType::RT_SUM,MLSL::GroupType::GT_DATA>(
+                send_buff, recv_buff, buf_size);
+          bool complete = false;
+          DLOG(INFO) << "Launch reduce with param id " << param_id;
+          // progress communication ASAP
+          MLSL::Environment::GetEnv().Test(reduce_req_vec[param_id], &complete);
+          if (complete) {
+            // reset req to indicate no need to do Wait
+            reduce_req_vec[param_id] = NULL;
+          }
+        }
+
+        if (distrib.is_root(MLSL::GroupType::GT_DATA)) {
+          AsyncTask req_task(layer_id, param_id, NULL);
+          reduce_req_list.push_back(req_task);
+        }
       }
     }
 
@@ -298,13 +328,17 @@ namespace caffe {
         else {
           MLSL::Environment::GetEnv().Test(reduce_req_vec[iter->param_id], &complete);
         }
+
         if (complete) {
           // reset req to indicate no need to do Wait
           reduce_req_vec[iter->param_id] = NULL;
 
+          int param_id = iter->param_id;
+          DLOG(INFO) << "Finish reduce on param id " << param_id
+            << " value " << net_params[param_id]->sumsq_diff();
+
           void* send_buff;
           void* recv_buff;
-          int param_id = iter->param_id;
           size_t buf_size = net_params[param_id]->count();
           
           if (CAN_USE_PRV_DIFF(net_params[param_id] ) ) {
@@ -319,6 +353,7 @@ namespace caffe {
           else {
             recv_buff = (void*)net_params[param_id]->mutable_cpu_data();
           }
+
           mn::Distribution &distrib = layers[iter->layer_id]->GetDistribution();
           int server_mpi_rank = mn::param_to_server_rank(iter->layer_id, iter->param_id);
           mn::TaskRequest task(
@@ -328,17 +363,24 @@ namespace caffe {
           int tag = task.GetTag();
           MPI_Request send_req;
           int recv_flag = 1;
-           // recv from PS
+          // recv from PS
           MPI_Irecv(recv_buff, buf_size, mn::DtypeToMPIDtype<Dtype>(),
                     server_mpi_rank, tag, MPI_COMM_WORLD, &irecv_req_vec[param_id]);
           MPI_Test(&irecv_req_vec[param_id], &recv_flag, MPI_STATUS_IGNORE);
           CHECK(!recv_flag);
+          DLOG(INFO) << "Receive data of param id " << param_id
+            << " from PS (rank " << server_mpi_rank << " and tag " << tag 
+            << ")";
           // Send to PS
           MPI_Isend(send_buff, buf_size, mn::DtypeToMPIDtype<Dtype>(),
                     server_mpi_rank, tag, MPI_COMM_WORLD, &send_req);
           // TODO: why do we have to wait here?
           MPI_Wait(&send_req, MPI_STATUS_IGNORE);
 
+          DLOG(INFO) << "Send to PS of param id " << param_id
+            << " to PS (rank " << server_mpi_rank << " and tag " << tag
+            << ")";
+
           irecv_req_list.push_back(task);
           iter = reduce_req_list.erase(iter);
         }
@@ -346,7 +388,10 @@ namespace caffe {
       }
     }
 
-    void launch_param_broadcast(int layer_id, int param_id) {
+    void launch_param_broadcast(int param_id) {
+      if (mn::get_nodes_count() <= 1)
+        return;
+
       Dtype* buff;
       if (CAN_USE_PRV_DATA(net_params[param_id])) {
         if (distrib_bcast->is_root(MLSL::GroupType::GT_DATA))
@@ -361,6 +406,7 @@ namespace caffe {
           buff = net_params[param_id]->mutable_cpu_data();
       }
       size_t buf_size = net_params[param_id]->count();
+      DLOG(INFO) << "Launch broadcast request with param id " << param_id;
       broadcast_req_vec[param_id] =
           distrib_bcast->bcast_async<Dtype,MLSL::GroupType::GT_DATA>(buff, buf_size);
     }
@@ -376,6 +422,8 @@ namespace caffe {
           MPI_Test(&irecv_req_vec[param_id], &flag, MPI_STATUS_IGNORE);
         }
         if (flag) {
+          DLOG(INFO) << " Root node received model param id " << param_id 
+            << " value " << net_params[param_id]->sumsq_data();
           irecv_req_vec[param_id] = MPI_REQUEST_NULL;
           irecv_done[param_id] = true;
           iter = irecv_req_list.erase(iter);
@@ -392,7 +440,7 @@ namespace caffe {
           int param_id = layer_param_ids[i][j];
           if (!broadcast_launched[param_id]) {
             if (irecv_done[param_id]) {
-              launch_param_broadcast(i, param_id);
+              launch_param_broadcast(param_id);
               broadcast_launched[param_id] = true;
             } else return;
           }
@@ -402,61 +450,83 @@ namespace caffe {
 
     void on_backward_finished(int layer_id) {
       boost::shared_ptr<Layer<Dtype>> &layer = layers[layer_id];
-      if (layer->layerOp == nullptr) {
-        return;
-      }
 
       if (mn::use_param_server()) {
-        std::vector<int> &param_ids = layer_param_ids[layer_id];
-        // TODO: descending is faster?
-        for (int i = param_ids.size() - 1; i >= 0; --i) {
-          if (!layer->ParamNeedReduce(i)) continue;
-          launch_reduce(layer_id, param_ids[i]);
-          mn::Distribution &distrib = layer->GetDistribution();
-          if (distrib.is_root(MLSL::GroupType::GT_DATA)) {
-            check_and_launch_comm_to_ps();
-            check_and_launch_broadcast();
-          } else {
-            launch_param_broadcast(layer_id, param_ids[i]);
+        launch_reduce(layer_id);
+
+        mn::Distribution &distrib = layer->GetDistribution();
+        if (distrib.is_root(MLSL::GroupType::GT_DATA)) {
+          check_and_launch_comm_to_ps();
+          check_and_launch_broadcast();
+        } else {
+          std::vector<int> &param_ids = layer_param_ids[layer_id];
+          for (int i=param_ids.size() - 1; i >= 0; --i) {
+            launch_param_broadcast(param_ids[i]);
           }
         }
       } else {
+        if (layer->layerOp == nullptr) {
+          return;
+        }
         launch_allreduce(layer_id);
       }
     }
 
+
+
     void delwt_wait_ps(int layer_id) {
       mn::Distribution &distrib = layers[layer_id]->GetDistribution();
+
+      std::vector<int> &param_ids = layer_param_ids[layer_id];
       if (distrib.is_root(MLSL::GroupType::GT_DATA)) {
-        std::vector<int> &param_ids = layer_param_ids[layer_id];
-        // TODO: can we start comm with ps earlier? Per-layer data would be inconsistent then.
-        check_and_launch_comm_to_ps();
-        check_and_launch_broadcast();
-        for (int i = param_ids.size() - 1; i >= 0; i--) {
-          int param_id = param_ids[i];
-          // wait for reduce
-          if (reduce_req_vec[param_id] != NULL) {
-            MLSL::Environment::GetEnv().Wait(reduce_req_vec[param_id]);
-          }
-          reduce_req_vec[param_id] = NULL;
-          // wait for new param from param server
-          if (irecv_req_vec[param_id] != MPI_REQUEST_NULL) {
-            MPI_Wait(&irecv_req_vec[param_id], MPI_STATUS_IGNORE);
-            // the req is set to MPI_Request_NULL indicating the request is already finished
-            irecv_req_vec[param_id] = MPI_REQUEST_NULL;
+        bool bcast_launched = false;
+        while (!bcast_launched) {
+          // TODO: can we start comm with ps earlier? Per-layer data would be inconsistent then.
+          check_and_launch_comm_to_ps();
+          check_and_launch_broadcast();
+          bcast_launched = true;
+          for (int i = param_ids.size() - 1; i >= 0; i--) {
+            bcast_launched = bcast_launched & broadcast_launched[param_ids[i]];
           }
-          irecv_done[param_id] = false;
-          // wait for the completion of broadcast
-          if (broadcast_req_vec[param_id] != NULL) {
-            MLSL::Environment::GetEnv().Wait(broadcast_req_vec[param_id]);
-            broadcast_req_vec[param_id] = NULL;
-          }
-          broadcast_launched[param_id] = false;
         }
       }
+
 #ifdef FW_OVERLAP_OPT
       solver->set_layer_finished_flag(layer_id, true);
 #endif
+
+      for (int i = param_ids.size() - 1; i >= 0; i--) {
+        int param_id = param_ids[i];
+        DLOG(INFO) << " Wait reduce layer id " << layer_id << " param id " << param_id;
+        // wait for reduce
+        if (reduce_req_vec[param_id] != NULL) {
+          MLSL::Environment::GetEnv().Wait(reduce_req_vec[param_id]);
+        }
+        reduce_req_vec[param_id] = NULL;
+          
+        DLOG(INFO) << "Worker (iter " << solver->root_solver()->iter() << "): param id=" << param_id << " weight=" << net_params[param_id]->sumsq_diff();
+
+        DLOG(INFO) << " Wait recv layer id " << layer_id << " param id " << param_id;
+
+        // wait for new param from param server
+        if (irecv_req_vec[param_id] != MPI_REQUEST_NULL) {
+          MPI_Wait(&irecv_req_vec[param_id], MPI_STATUS_IGNORE);
+          // the req is set to MPI_Request_NULL indicating the request is already finished
+          irecv_req_vec[param_id] = MPI_REQUEST_NULL;
+        }
+        irecv_done[param_id] = false;
+
+        DLOG(INFO) << " Wait broadcast layer id " << layer_id << " param id " << param_id;
+
+        // wait for the completion of broadcast
+        if (broadcast_req_vec[param_id] != NULL) {
+          MLSL::Environment::GetEnv().Wait(broadcast_req_vec[param_id]);
+          broadcast_req_vec[param_id] = NULL;
+        }
+        broadcast_launched[param_id] = false;
+
+        DLOG(INFO) << "Worker (iter " << solver->root_solver()->iter() << "): param id=" << param_id << " data=" << net_params[param_id]->sumsq_data();
+      }
     }
 
     void delwt_wait_no_ps(int layer_id) {
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index f27f24473..0d69f671b 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -287,7 +287,7 @@ class Net {
   *        then scale layer can be dropped
   */
   // TODO: Make it decent C++ anonymous function etc.
-  static void CompilationRuleOne(const NetParameter& param,
+  static void CompilationRuleRemoveScale(const NetParameter& param,
                                  NetParameter* param_compiled);
   /**
   * @brief This is rule that analyze layer if it is of type MKLDNNReLU and if that is the case
@@ -296,7 +296,7 @@ class Net {
   */
 
 
-  static void CompilationRuleTwo(const NetParameter& param,
+  static void CompilationRuleConvReluFusion(const NetParameter& param,
                                  NetParameter* param_compiled);
 
   /**
@@ -304,14 +304,29 @@ class Net {
   *        and is to perform in place computation 
   *        if positive then make it doing out-ofplace computation
   */
-  static void CompilationRuleThree(const NetParameter& param,
+  static void CompilationRuleBNInplace(const NetParameter& param,
                              NetParameter* param_compiled);
 
   /**
   * @brief This is rule analyze for conv/elt/relu fusion.
   */
-  static void CompilationRuleFour(const NetParameter& param,
+  static void CompilationRuleConvSumFusion(const NetParameter& param,
                              NetParameter* param_compiled);
+
+   /**
+  * @brief This is rule analyze for general sparse.
+  */
+  static void CompilationRuleSparse(const NetParameter& param,
+                             NetParameter* param_compiled);
+
+  /**
+  * @brief This is rule that analyze layer if it is of type MKLDNNReLU and if that is the case
+  *        and previous layer which serves as input layer to MKLDNNReLU Layer is MKLDNNBN
+  *        then MKLDNNReLU layer can be dropped
+  */
+  static void CompilationRuleFuseBnRelu(const NetParameter& param,
+                                 NetParameter* param_compiled);
+
   /**
    * @brief If find "Conv--BN--Scale" in current network, merge BN and Scale layer into Convolution
    * layers, this optimization only works in caffe TEST phase now.
diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
index 89df578a6..3e950ede1 100644
--- a/include/caffe/sgd_solvers.hpp
+++ b/include/caffe/sgd_solvers.hpp
@@ -143,8 +143,8 @@ class RMSPropSolver : public SGDSolver<Dtype> {
  protected:
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
   void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.momentum())
-        << "Momentum cannot be used with RMSProp.";
+//    CHECK_EQ(0, this->param_.momentum())
+//        << "Momentum cannot be used with RMSProp.";
     CHECK_GE(this->param_.rms_decay(), 0)
         << "rms_decay should lie between 0 and 1.";
     CHECK_LT(this->param_.rms_decay(), 1)
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 05ec1ecd8..5b6d869ed 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -130,6 +130,10 @@ struct PrvMemDescr {
     PRV_DESCR_MKLDNN
   };
   virtual PrvDescrType get_descr_type() = 0;
+#ifdef CO_SIM
+    virtual void create_reorder_from_prv_cosim(void* cpu_ptr) = 0;
+    virtual void convert_from_prv_cosim(void* cpu_ptr) = 0;
+#endif
 };
 
 /**
@@ -144,12 +148,20 @@ class SyncedMemory {
       : cpu_ptr_(NULL), gpu_ptr_(NULL),
         size_(0), head_(UNINITIALIZED), own_cpu_data_(false),
         cpu_malloc_use_cuda_(false), own_gpu_data_(false), own_prv_data_(false),
-        gpu_device_(-1) {}
+        gpu_device_(-1)
+#ifdef CO_SIM
+        , cpu_ptr_cosim_(NULL), own_cpu_data_cosim_(false)
+#endif
+        {}
   explicit SyncedMemory(size_t size)
       : cpu_ptr_(NULL), gpu_ptr_(NULL),
         size_(size), head_(UNINITIALIZED), own_cpu_data_(false),
         cpu_malloc_use_cuda_(false), own_gpu_data_(false), own_prv_data_(false),
-        gpu_device_(-1) {}
+        gpu_device_(-1)
+#ifdef CO_SIM
+        , cpu_ptr_cosim_(NULL), own_cpu_data_cosim_(false)
+#endif
+        {}
   ~SyncedMemory();
   void swap(shared_ptr<SyncedMemory> other);
   const void* cpu_data();
@@ -159,6 +171,9 @@ class SyncedMemory {
   void* mutable_cpu_data();
   void* mutable_gpu_data();
 
+#ifdef CO_SIM
+    const void * cpu_data_cosim();
+#endif
   const void* cpu_ptr() const { return cpu_ptr_; }
 
   shared_ptr<PrvMemDescr> prv_descriptor_;
@@ -187,7 +202,11 @@ class SyncedMemory {
   bool own_prv_data_;
   int gpu_device_;
   boost::mutex mtx;
-
+#ifdef CO_SIM
+  void* cpu_ptr_cosim_;
+  void to_cpu_cosim();
+  bool own_cpu_data_cosim_;
+#endif
   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
 };  // class SyncedMemory
 
diff --git a/mkldnn.commit b/mkldnn.commit
index 4925f6349..ab7cef75c 100644
--- a/mkldnn.commit
+++ b/mkldnn.commit
@@ -1 +1 @@
-ae00102be506ed0fe2099c6557df2aa88ad57ec1
\ No newline at end of file
+464c268e544bae26f9b85a2acb9122c766a4c396
diff --git a/models/default_resnet_50/train_val.prototxt b/models/default_resnet_50/train_val.prototxt
index 9b251c7f4..d81325c6f 100644
--- a/models/default_resnet_50/train_val.prototxt
+++ b/models/default_resnet_50/train_val.prototxt
@@ -3036,13 +3036,40 @@ layer {
 	  phase: TRAIN
 	}
 }
+#layer {
+#       name: "probt"
+#       type: "Softmax"
+#       bottom: "fc1000"
+#       top: "probt"
+#       include {
+#	   phase: TEST
+#	}
+#}
+layer {
+	name: "accuracy/top-1"
+	type: "Accuracy"
+	bottom: "fc1000"
+	bottom: "label"
+	top: "accuracy/top-1"
+	accuracy_param
+	{
+		top_k: 1
+	}
+	include {
+		phase: TEST
+	}
+}
 layer {
-       name: "probt"
-       type: "Softmax"
-       bottom: "fc1000"
-       top: "probt"
-       include {
-	   phase: TEST
+	name: "accuracy/top-5"
+	type: "Accuracy"
+	bottom: "fc1000"
+	bottom: "label"
+	top: "accuracy/top-5"
+	accuracy_param
+	{
+		top_k: 5
+	}
+	include {
+		phase: TEST
 	}
 }
-		
diff --git a/models/intel_optimized_models/alexnet/bdw/solver_dummydata.prototxt b/models/intel_optimized_models/alexnet/bdw/solver_dummydata.prototxt
deleted file mode 100644
index 79e5061f9..000000000
--- a/models/intel_optimized_models/alexnet/bdw/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/alexnet/bdw/alexnet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/alexnet/knm/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/knm/train_val_dummydata.prototxt
deleted file mode 100644
index 36cb0de76..000000000
--- a/models/intel_optimized_models/alexnet/knm/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,394 +0,0 @@
-name: "AlexNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 1024 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 1024 dim: 1 dim: 1   dim: 1   }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 1024 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 1024 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "norm1"
-  type: "LRN"
-  bottom: "conv1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "norm1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "norm2"
-  type: "LRN"
-  bottom: "conv2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "norm2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "conv4"
-  type: "Convolution"
-  bottom: "conv3"
-  top: "conv4"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "conv4"
-  top: "conv4"
-}
-layer {
-  name: "conv5"
-  type: "Convolution"
-  bottom: "conv4"
-  top: "conv5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu5"
-  type: "ReLU"
-  bottom: "conv5"
-  top: "conv5"
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "fc6"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc6"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu6"
-  type: "ReLU"
-  bottom: "fc6"
-  top: "fc6"
-}
-layer {
-  name: "drop6"
-  type: "Dropout"
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc7"
-  type: "InnerProduct"
-  bottom: "fc6"
-  top: "fc7"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu7"
-  type: "ReLU"
-  bottom: "fc7"
-  top: "fc7"
-}
-layer {
-  name: "drop7"
-  type: "Dropout"
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc8"
-  type: "InnerProduct"
-  bottom: "fc7"
-  top: "fc8"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/alexnet/skx/solver_dummydata.prototxt b/models/intel_optimized_models/alexnet/skx/solver_dummydata.prototxt
deleted file mode 100644
index d7b5a2732..000000000
--- a/models/intel_optimized_models/alexnet/skx/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/alexnet/skx/alexnet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt
deleted file mode 100644
index bc064140e..000000000
--- a/models/intel_optimized_models/alexnet/skx/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,394 +0,0 @@
-name: "AlexNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 1024 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 1024 dim: 1 dim: 1   dim: 1   }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 512 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 512 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "norm1"
-  type: "LRN"
-  bottom: "conv1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "norm1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "norm2"
-  type: "LRN"
-  bottom: "conv2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "norm2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "conv4"
-  type: "Convolution"
-  bottom: "conv3"
-  top: "conv4"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "conv4"
-  top: "conv4"
-}
-layer {
-  name: "conv5"
-  type: "Convolution"
-  bottom: "conv4"
-  top: "conv5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu5"
-  type: "ReLU"
-  bottom: "conv5"
-  top: "conv5"
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "fc6"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc6"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu6"
-  type: "ReLU"
-  bottom: "fc6"
-  top: "fc6"
-}
-layer {
-  name: "drop6"
-  type: "Dropout"
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc7"
-  type: "InnerProduct"
-  bottom: "fc6"
-  top: "fc7"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu7"
-  type: "ReLU"
-  bottom: "fc7"
-  top: "fc7"
-}
-layer {
-  name: "drop7"
-  type: "Dropout"
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc8"
-  type: "InnerProduct"
-  bottom: "fc7"
-  top: "fc8"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/alexnet/train_val_dummydata.prototxt b/models/intel_optimized_models/alexnet/train_val_dummydata.prototxt
deleted file mode 100644
index fc47551c3..000000000
--- a/models/intel_optimized_models/alexnet/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,394 +0,0 @@
-name: "AlexNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "norm1"
-  type: "LRN"
-  bottom: "conv1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "norm1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "norm2"
-  type: "LRN"
-  bottom: "conv2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "norm2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "conv4"
-  type: "Convolution"
-  bottom: "conv3"
-  top: "conv4"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "conv4"
-  top: "conv4"
-}
-layer {
-  name: "conv5"
-  type: "Convolution"
-  bottom: "conv4"
-  top: "conv5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu5"
-  type: "ReLU"
-  bottom: "conv5"
-  top: "conv5"
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "fc6"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc6"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu6"
-  type: "ReLU"
-  bottom: "fc6"
-  top: "fc6"
-}
-layer {
-  name: "drop6"
-  type: "Dropout"
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc7"
-  type: "InnerProduct"
-  bottom: "fc6"
-  top: "fc7"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "relu7"
-  type: "ReLU"
-  bottom: "fc7"
-  top: "fc7"
-}
-layer {
-  name: "drop7"
-  type: "Dropout"
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "fc8"
-  type: "InnerProduct"
-  bottom: "fc7"
-  top: "fc8"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/alexnet/knm/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/alexnet/solver.prototxt
similarity index 80%
rename from models/intel_optimized_models/alexnet/knm/solver_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/alexnet/solver.prototxt
index 9cf1543c2..7cf67a129 100644
--- a/models/intel_optimized_models/alexnet/knm/solver_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/alexnet/solver.prototxt
@@ -10,16 +10,14 @@
 #Top-5: 80.4%
 #Top-1: 57.4%
 #Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/alexnet/knm/train_val_dummydata.prototxt"
+net: "models/intel_optimized_models/benchmark/alexnet/train_val.prototxt"
 test_iter: 1000
 test_interval: 10000
 base_lr: 0.007
 lr_policy: "poly"
 power: 0.6
-display: 1
-max_iter: 5000
+display: 20
+max_iter: 250000
 momentum: 0.9
 weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/alexnet/knm/alexnet_train"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/alexnet/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/alexnet/solver_dummydata.prototxt
similarity index 83%
rename from models/intel_optimized_models/alexnet/solver_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/alexnet/solver_dummydata.prototxt
index 5c15e565e..8bf2a9bd4 100644
--- a/models/intel_optimized_models/alexnet/solver_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/alexnet/solver_dummydata.prototxt
@@ -10,7 +10,7 @@
 #Top-5: 80.4%
 #Top-1: 57.4%
 #Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/alexnet/train_val_dummydata.prototxt"
+net: "models/intel_optimized_models/benchmark/alexnet/train_val_dummydata.prototxt"
 test_iter: 1000
 test_interval: 10000
 base_lr: 0.007
@@ -20,6 +20,4 @@ display: 1
 max_iter: 200
 momentum: 0.9
 weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/alexnet/alexnet_train"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/alexnet/train_val.prototxt
similarity index 88%
rename from models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/alexnet/train_val.prototxt
index fc47551c3..588b4ea7c 100644
--- a/models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/alexnet/train_val.prototxt
@@ -1,39 +1,42 @@
 name: "AlexNet"
 layer {
   name: "data"
-  type: "DummyData"
+  type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TRAIN
   }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
   }
 }
 layer {
   name: "data"
-  type: "DummyData"
+  type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TEST
   }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 256 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
   }
 }
-
 layer {
   name: "conv1"
   type: "Convolution"
@@ -363,32 +366,19 @@ layer {
   }
 }
 layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
-layer {
-  name: "loss3/top-1"
+  name: "accuracy"
   type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
-  top: "loss3/top-1"
+  top: "accuracy"
   include {
     phase: TEST
   }
 }
 layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
+  name: "loss"
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
+  top: "loss"
 }
diff --git a/models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/alexnet/train_val_dummydata.prototxt
similarity index 100%
rename from models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/alexnet/train_val_dummydata.prototxt
diff --git a/models/intel_optimized_models/benchmark/googlenet/solver.prototxt b/models/intel_optimized_models/benchmark/googlenet/solver.prototxt
new file mode 100644
index 000000000..b3f23ec95
--- /dev/null
+++ b/models/intel_optimized_models/benchmark/googlenet/solver.prototxt
@@ -0,0 +1,13 @@
+net: "models/intel_optimized_models/benchmark/googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 10000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.012
+lr_policy: "poly"
+power: 0.5
+max_iter: 800000
+momentum: 0.9
+weight_decay: 0.0002
+solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet/solver_dummydata.prototxt
similarity index 82%
rename from models/intel_optimized_models/googlenet/solver_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet/solver_dummydata.prototxt
index 02ce2018a..f6581d24d 100644
--- a/models/intel_optimized_models/googlenet/solver_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/googlenet/solver_dummydata.prototxt
@@ -10,7 +10,7 @@
 #Top-5: 80.4%
 #Top-1: 57.4%
 #Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet/train_val_dummydata.prototxt"
+net: "models/intel_optimized_models/benchmark/googlenet/train_val_dummydata.prototxt"
 test_iter: 1000
 test_interval: 10000
 base_lr: 0.007
@@ -20,6 +20,4 @@ display: 1
 max_iter: 200
 momentum: 0.9
 weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet/googlenet_train"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet/train_val.prototxt
similarity index 96%
rename from models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet/train_val.prototxt
index 602416008..6f9b6fded 100644
--- a/models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/googlenet/train_val.prototxt
@@ -1,40 +1,46 @@
 name: "GoogleNet"
 layer {
   name: "data"
-  type: "DummyData"
+  type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TRAIN
   }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 96
+    backend: LMDB
   }
 }
-
 layer {
   name: "data"
-  type: "DummyData"
+  type: "Data"
   top: "data"
   top: "label"
   include {
     phase: TEST
   }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
   }
 }
-
 layer {
   name: "conv1/7x7_s2"
   type: "Convolution"
@@ -58,7 +64,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -111,7 +117,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -143,7 +149,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -196,7 +202,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -227,7 +233,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -259,7 +265,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -290,7 +296,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -322,7 +328,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -365,7 +371,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -405,7 +411,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -436,7 +442,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -468,7 +474,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -499,7 +505,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -531,7 +537,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -574,7 +580,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -625,7 +631,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -656,7 +662,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -688,7 +694,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -719,7 +725,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -751,7 +757,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -794,7 +800,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -845,7 +851,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -875,7 +881,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -970,7 +976,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1001,7 +1007,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1033,7 +1039,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1064,7 +1070,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1096,7 +1102,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1139,7 +1145,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1179,7 +1185,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1210,7 +1216,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1242,7 +1248,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1273,7 +1279,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1305,7 +1311,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1348,7 +1354,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1388,7 +1394,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1419,7 +1425,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1451,7 +1457,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1482,7 +1488,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1514,7 +1520,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1557,7 +1563,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1608,7 +1614,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1638,7 +1644,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1733,7 +1739,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1764,7 +1770,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1796,7 +1802,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1827,7 +1833,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1859,7 +1865,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1902,7 +1908,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1953,7 +1959,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -1984,7 +1990,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2016,7 +2022,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2047,7 +2053,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2079,7 +2085,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2122,7 +2128,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2162,7 +2168,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2193,7 +2199,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2225,7 +2231,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2256,7 +2262,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2288,7 +2294,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
@@ -2331,7 +2337,7 @@ layer {
     }
     bias_filler {
       type: "constant"
-      value: 0.1
+      value: 0.2
     }
   }
 }
diff --git a/models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet/train_val_dummydata.prototxt
similarity index 100%
rename from models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet/train_val_dummydata.prototxt
diff --git a/models/intel_optimized_models/benchmark/googlenet_v2/solver.prototxt b/models/intel_optimized_models/benchmark/googlenet_v2/solver.prototxt
new file mode 100644
index 000000000..7dff4c658
--- /dev/null
+++ b/models/intel_optimized_models/benchmark/googlenet_v2/solver.prototxt
@@ -0,0 +1,14 @@
+net: "models/intel_optimized_models/benchmark/googlenet_v2/train_val.prototxt"
+test_iter: 1000
+test_interval: 10000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.045
+lr_policy: "step"
+stepsize: 2133
+gamma: 0.96
+max_iter: 400000
+momentum: 0.9
+weight_decay: 0.0002
+solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet_v2/solver_dummydata.prototxt
similarity index 82%
rename from models/intel_optimized_models/resnet_50/solver_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet_v2/solver_dummydata.prototxt
index 3ba69689f..736c3f7a6 100644
--- a/models/intel_optimized_models/resnet_50/solver_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/googlenet_v2/solver_dummydata.prototxt
@@ -10,7 +10,7 @@
 #Top-5: 80.4%
 #Top-1: 57.4%
 #Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/resnet_50/train_val_dummydata.prototxt"
+net: "models/intel_optimized_models/benchmark/googlenet_v2/train_val_dummydata.prototxt"
 test_iter: 1000
 test_interval: 10000
 base_lr: 0.007
@@ -20,6 +20,4 @@ display: 1
 max_iter: 5000
 momentum: 0.9
 weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/resnet_50/resnet_50_train"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet_v2/train_val.prototxt
similarity index 99%
rename from models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet_v2/train_val.prototxt
index 28a89b023..e54132adb 100644
--- a/models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/googlenet_v2/train_val.prototxt
@@ -2,44 +2,54 @@
 name: "InceptionNetwork"
 ### Training Set
 layer {
-  name: "data"
-  type: "DummyData"
   top: "data"
   top: "label"
+  name: "data"
+  type: "Data"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 96
+    backend: LMDB
+#    shuffle: true
+  }
   include {
     phase: TRAIN
   }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 96 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 96 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: true
+    crop_size: 224
+#    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
   }
 }
-
 ### Validation Set
 layer {
-  name: "data"
-  type: "DummyData"
   top: "data"
   top: "label"
+  name: "data"
+  type: "Data"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 32
+    backend: LMDB
+  }
   include {
     phase: TEST
   }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 192 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 192 dim: 1 dim: 1   dim: 1   }
+  transform_param {
+    mirror: false
+    crop_size: 224
+#    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
   }
 }
 
 layer {
-  bottom: "data"
+ bottom: "data"
   top: "conv1/7x7_s2"
   name: "conv1/7x7_s2"
   type: "Convolution"
diff --git a/models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet_v2/train_val_dummydata.prototxt
similarity index 100%
rename from models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/googlenet_v2/train_val_dummydata.prototxt
diff --git a/models/intel_optimized_models/resnet_50/solver.prototxt b/models/intel_optimized_models/benchmark/resnet_50/solver.prototxt
similarity index 86%
rename from models/intel_optimized_models/resnet_50/solver.prototxt
rename to models/intel_optimized_models/benchmark/resnet_50/solver.prototxt
index 5693483fd..8f3161e6f 100644
--- a/models/intel_optimized_models/resnet_50/solver.prototxt
+++ b/models/intel_optimized_models/benchmark/resnet_50/solver.prototxt
@@ -1,5 +1,5 @@
 #The prototxt files(solver.prototxt and train_val.prototxt) are copied from "models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch", temporarily used for throughput benchmarking. It can't achieve expected accuracy for training. We'll provide the files for single node training in the future.
-net: "models/intel_optimized_models/resnet_50/train_val.prototxt"
+net: "models/intel_optimized_models/benchmark/resnet_50/train_val.prototxt"
 test_iter: 1000
 test_interval: 625
 test_initialization: false
diff --git a/models/intel_optimized_models/alexnet/knl/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/resnet_50/solver_dummydata.prototxt
similarity index 82%
rename from models/intel_optimized_models/alexnet/knl/solver_dummydata.prototxt
rename to models/intel_optimized_models/benchmark/resnet_50/solver_dummydata.prototxt
index b10739bff..61454437d 100644
--- a/models/intel_optimized_models/alexnet/knl/solver_dummydata.prototxt
+++ b/models/intel_optimized_models/benchmark/resnet_50/solver_dummydata.prototxt
@@ -10,7 +10,7 @@
 #Top-5: 80.4%
 #Top-1: 57.4%
 #Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/alexnet/knl/train_val_dummydata.prototxt"
+net: "models/intel_optimized_models/benchmark/resnet_50/train_val_dummydata.prototxt"
 test_iter: 1000
 test_interval: 10000
 base_lr: 0.007
@@ -20,6 +20,4 @@ display: 1
 max_iter: 5000
 momentum: 0.9
 weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/alexnet/knl/alexnet_train"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/train_val.prototxt b/models/intel_optimized_models/benchmark/resnet_50/train_val.prototxt
similarity index 99%
rename from models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/train_val.prototxt
rename to models/intel_optimized_models/benchmark/resnet_50/train_val.prototxt
index 3dd57aaac..de4efad2b 100644
--- a/models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/train_val.prototxt
+++ b/models/intel_optimized_models/benchmark/resnet_50/train_val.prototxt
@@ -783,7 +783,7 @@ layer {
     num_output: 128
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -841,7 +841,7 @@ layer {
     num_output: 128
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -1569,7 +1569,7 @@ layer {
     num_output: 256
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -1627,7 +1627,7 @@ layer {
     num_output: 256
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -2727,7 +2727,7 @@ layer {
     num_output: 512
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -2785,7 +2785,7 @@ layer {
     num_output: 512
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -3302,6 +3302,9 @@ layer {
   top: "loss"
   name: "prob"
   type: "SoftmaxWithLoss"
+  include {
+      phase: TRAIN
+  }
 }
 layer {
   name: "loss3/top-1"
diff --git a/models/intel_optimized_models/multinode/resnet_50_8_nodes/train_val.prototxt b/models/intel_optimized_models/benchmark/resnet_50/train_val_dummydata.prototxt
similarity index 92%
rename from models/intel_optimized_models/multinode/resnet_50_8_nodes/train_val.prototxt
rename to models/intel_optimized_models/benchmark/resnet_50/train_val_dummydata.prototxt
index 75bff6269..d9172b0c9 100644
--- a/models/intel_optimized_models/multinode/resnet_50_8_nodes/train_val.prototxt
+++ b/models/intel_optimized_models/benchmark/resnet_50/train_val_dummydata.prototxt
@@ -1,3322 +1,3298 @@
-name: "ResNet-50"
-bn_stats_batch_size: 32
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  transform_param {
-    mirror: true
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_aspect_ratio_param {
-      min_area_ratio: 0.08
-      max_area_ratio: 1
-      aspect_ratio_change: 0.75
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 128
-    backend: LMDB
-    prefetch: 2
-    shuffle: true
-  }
-}
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  transform_param {
-    mirror: false
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_resize_param {
-      min_size: 256
-      max_size: 256
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    batch_size: 50
-    backend: LMDB
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1"
-  name: "conv1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 7
-    pad: 3
-    stride: 2
-    weight_filler {
-      type: "msra"
-      variance_norm: FAN_OUT
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "bn_conv1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "scale_conv1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "conv1_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "pool1"
-  name: "pool1"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 3
-    stride: 2
-    pool: MAX
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch1"
-  name: "res2a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "bn2a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "scale2a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "bn2a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "scale2a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "bn2a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "scale2a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2c"
-  name: "res2a_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "bn2a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "scale2a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  bottom: "res2a_branch2c"
-  top: "res2a"
-  name: "res2a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2a"
-  name: "res2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "bn2b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "scale2b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "bn2b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "scale2b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2c"
-  name: "res2b_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "bn2b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "scale2b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a"
-  bottom: "res2b_branch2c"
-  top: "res2b"
-  name: "res2b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2b"
-  name: "res2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "bn2c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "scale2c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "bn2c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "scale2c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2c"
-  name: "res2c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "bn2c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "scale2c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b"
-  bottom: "res2c_branch2c"
-  top: "res2c"
-  name: "res2c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res2c"
-  name: "res2c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch1"
-  name: "res3a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "bn3a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "scale3a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "bn3a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "scale3a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "bn3a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "scale3a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2c"
-  name: "res3a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "bn3a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "scale3a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  bottom: "res3a_branch2c"
-  top: "res3a"
-  name: "res3a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3a"
-  name: "res3a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "bn3b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "scale3b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "bn3b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "scale3b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2c"
-  name: "res3b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "bn3b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "scale3b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a"
-  bottom: "res3b_branch2c"
-  top: "res3b"
-  name: "res3b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3b"
-  name: "res3b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "bn3c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "scale3c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "bn3c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "scale3c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2c"
-  name: "res3c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "bn3c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "scale3c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b"
-  bottom: "res3c_branch2c"
-  top: "res3c"
-  name: "res3c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3c"
-  name: "res3c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "bn3d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "scale3d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "bn3d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "scale3d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2c"
-  name: "res3d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "bn3d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "scale3d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c"
-  bottom: "res3d_branch2c"
-  top: "res3d"
-  name: "res3d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res3d"
-  name: "res3d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch1"
-  name: "res4a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "bn4a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "scale4a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "bn4a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "scale4a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "bn4a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "scale4a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2c"
-  name: "res4a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "bn4a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "scale4a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  bottom: "res4a_branch2c"
-  top: "res4a"
-  name: "res4a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4a"
-  name: "res4a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "bn4b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "scale4b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "bn4b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "scale4b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2c"
-  name: "res4b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "bn4b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "scale4b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a"
-  bottom: "res4b_branch2c"
-  top: "res4b"
-  name: "res4b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4b"
-  name: "res4b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "bn4c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "scale4c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "bn4c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "scale4c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2c"
-  name: "res4c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "bn4c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "scale4c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b"
-  bottom: "res4c_branch2c"
-  top: "res4c"
-  name: "res4c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4c"
-  name: "res4c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "bn4d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "scale4d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "bn4d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "scale4d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2c"
-  name: "res4d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "bn4d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "scale4d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c"
-  bottom: "res4d_branch2c"
-  top: "res4d"
-  name: "res4d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4d"
-  name: "res4d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "bn4e_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "scale4e_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "bn4e_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "scale4e_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2c"
-  name: "res4e_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "bn4e_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "scale4e_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d"
-  bottom: "res4e_branch2c"
-  top: "res4e"
-  name: "res4e"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4e"
-  name: "res4e_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "bn4f_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "scale4f_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "bn4f_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "scale4f_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2c"
-  name: "res4f_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "bn4f_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "scale4f_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e"
-  bottom: "res4f_branch2c"
-  top: "res4f"
-  name: "res4f"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res4f"
-  name: "res4f_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch1"
-  name: "res5a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "bn5a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "scale5a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "bn5a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "scale5a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "bn5a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "scale5a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2c"
-  name: "res5a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "bn5a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "scale5a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  bottom: "res5a_branch2c"
-  top: "res5a"
-  name: "res5a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5a"
-  name: "res5a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "bn5b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "scale5b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "bn5b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "scale5b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2c"
-  name: "res5b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "bn5b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "scale5b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a"
-  bottom: "res5b_branch2c"
-  top: "res5b"
-  name: "res5b"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5b"
-  name: "res5b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "bn5c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "scale5c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "bn5c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "scale5c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2c"
-  name: "res5c_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "bn5c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "scale5c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b"
-  bottom: "res5c_branch2c"
-  top: "res5c"
-  name: "res5c"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "res5c"
-  name: "res5c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "pool5"
-  name: "pool5"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 7
-    stride: 1
-    pool: AVE
-  }
-}
-
-layer {
-  bottom: "pool5"
-  top: "fc1000"
-  name: "fc1000"
-  type: "InnerProduct"
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss"
-  name: "prob"
-  type: "SoftmaxWithLoss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  accuracy_param {
-    top_k: 5
-  }
-}
+name: "ResNet-50"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1 dim: 1  }
+  }
+}
+
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
+    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
+  }
+}
+
+layer {
+  bottom: "data"
+  top: "conv1"
+  name: "conv1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 7
+    pad: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+      variance_norm: FAN_OUT
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "bn_conv1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "scale_conv1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "conv1_relu"
+  type: "ReLU"
+  relu_param {
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "pool1"
+  name: "pool1"
+  type: "Pooling"
+  pooling_param {
+    kernel_size: 3
+    stride: 2
+    pool: MAX
+  }
+}
+
+layer {
+  bottom: "pool1"
+  top: "res2a_branch1"
+  name: "res2a_branch1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  name: "bn2a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  name: "scale2a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "pool1"
+  top: "res2a_branch2a"
+  name: "res2a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "bn2a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "scale2a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "res2a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2b"
+  name: "res2a_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "bn2a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "scale2a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "res2a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2c"
+  name: "res2a_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2c"
+  top: "res2a_branch2c"
+  name: "bn2a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2c"
+  top: "res2a_branch2c"
+  name: "scale2a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  bottom: "res2a_branch2c"
+  top: "res2a"
+  name: "res2a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a"
+  top: "res2a"
+  name: "res2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a"
+  top: "res2b_branch2a"
+  name: "res2b_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "bn2b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "scale2b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "res2b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2b"
+  name: "res2b_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "bn2b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "scale2b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "res2b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2c"
+  name: "res2b_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2c"
+  top: "res2b_branch2c"
+  name: "bn2b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2c"
+  top: "res2b_branch2c"
+  name: "scale2b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a"
+  bottom: "res2b_branch2c"
+  top: "res2b"
+  name: "res2b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b"
+  top: "res2b"
+  name: "res2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b"
+  top: "res2c_branch2a"
+  name: "res2c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "bn2c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "scale2c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "res2c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2b"
+  name: "res2c_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "bn2c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "scale2c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "res2c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2c"
+  name: "res2c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2c"
+  top: "res2c_branch2c"
+  name: "bn2c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2c"
+  top: "res2c_branch2c"
+  name: "scale2c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b"
+  bottom: "res2c_branch2c"
+  top: "res2c"
+  name: "res2c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res2c"
+  name: "res2c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res3a_branch1"
+  name: "res3a_branch1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  top: "res3a_branch1"
+  name: "bn3a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  top: "res3a_branch1"
+  name: "scale3a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res3a_branch2a"
+  name: "res3a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "bn3a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "scale3a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "res3a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2b"
+  name: "res3a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "bn3a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "scale3a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "res3a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2c"
+  name: "res3a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2c"
+  top: "res3a_branch2c"
+  name: "bn3a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2c"
+  top: "res3a_branch2c"
+  name: "scale3a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  bottom: "res3a_branch2c"
+  top: "res3a"
+  name: "res3a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a"
+  top: "res3a"
+  name: "res3a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a"
+  top: "res3b_branch2a"
+  name: "res3b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "bn3b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "scale3b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "res3b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2b"
+  name: "res3b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "bn3b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "scale3b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "res3b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2c"
+  name: "res3b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2c"
+  top: "res3b_branch2c"
+  name: "bn3b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2c"
+  top: "res3b_branch2c"
+  name: "scale3b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a"
+  bottom: "res3b_branch2c"
+  top: "res3b"
+  name: "res3b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b"
+  top: "res3b"
+  name: "res3b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b"
+  top: "res3c_branch2a"
+  name: "res3c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "bn3c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "scale3c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "res3c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2b"
+  name: "res3c_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "bn3c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "scale3c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "res3c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2c"
+  name: "res3c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2c"
+  top: "res3c_branch2c"
+  name: "bn3c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2c"
+  top: "res3c_branch2c"
+  name: "scale3c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b"
+  bottom: "res3c_branch2c"
+  top: "res3c"
+  name: "res3c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c"
+  top: "res3c"
+  name: "res3c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c"
+  top: "res3d_branch2a"
+  name: "res3d_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "bn3d_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "scale3d_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "res3d_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2b"
+  name: "res3d_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "bn3d_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "scale3d_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "res3d_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2c"
+  name: "res3d_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2c"
+  top: "res3d_branch2c"
+  name: "bn3d_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2c"
+  top: "res3d_branch2c"
+  name: "scale3d_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c"
+  bottom: "res3d_branch2c"
+  top: "res3d"
+  name: "res3d"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res3d"
+  name: "res3d_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res4a_branch1"
+  name: "res4a_branch1"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  name: "bn4a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  name: "scale4a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res4a_branch2a"
+  name: "res4a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "bn4a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "scale4a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "res4a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2b"
+  name: "res4a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "bn4a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "scale4a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "res4a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2c"
+  name: "res4a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2c"
+  top: "res4a_branch2c"
+  name: "bn4a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2c"
+  top: "res4a_branch2c"
+  name: "scale4a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  bottom: "res4a_branch2c"
+  top: "res4a"
+  name: "res4a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a"
+  top: "res4a"
+  name: "res4a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a"
+  top: "res4b_branch2a"
+  name: "res4b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "bn4b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "scale4b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "res4b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2b"
+  name: "res4b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "bn4b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "scale4b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "res4b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2c"
+  name: "res4b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2c"
+  top: "res4b_branch2c"
+  name: "bn4b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2c"
+  top: "res4b_branch2c"
+  name: "scale4b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a"
+  bottom: "res4b_branch2c"
+  top: "res4b"
+  name: "res4b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b"
+  top: "res4b"
+  name: "res4b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b"
+  top: "res4c_branch2a"
+  name: "res4c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "bn4c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "scale4c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "res4c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2b"
+  name: "res4c_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "bn4c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "scale4c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "res4c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2c"
+  name: "res4c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2c"
+  top: "res4c_branch2c"
+  name: "bn4c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2c"
+  top: "res4c_branch2c"
+  name: "scale4c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b"
+  bottom: "res4c_branch2c"
+  top: "res4c"
+  name: "res4c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c"
+  top: "res4c"
+  name: "res4c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c"
+  top: "res4d_branch2a"
+  name: "res4d_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "bn4d_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "scale4d_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "res4d_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2b"
+  name: "res4d_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "bn4d_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "scale4d_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "res4d_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2c"
+  name: "res4d_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2c"
+  top: "res4d_branch2c"
+  name: "bn4d_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2c"
+  top: "res4d_branch2c"
+  name: "scale4d_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c"
+  bottom: "res4d_branch2c"
+  top: "res4d"
+  name: "res4d"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d"
+  top: "res4d"
+  name: "res4d_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d"
+  top: "res4e_branch2a"
+  name: "res4e_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "bn4e_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "scale4e_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "res4e_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2b"
+  name: "res4e_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "bn4e_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "scale4e_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "res4e_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2c"
+  name: "res4e_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2c"
+  top: "res4e_branch2c"
+  name: "bn4e_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2c"
+  top: "res4e_branch2c"
+  name: "scale4e_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d"
+  bottom: "res4e_branch2c"
+  top: "res4e"
+  name: "res4e"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e"
+  top: "res4e"
+  name: "res4e_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e"
+  top: "res4f_branch2a"
+  name: "res4f_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "bn4f_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "scale4f_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "res4f_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2b"
+  name: "res4f_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "bn4f_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "scale4f_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "res4f_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2c"
+  name: "res4f_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2c"
+  top: "res4f_branch2c"
+  name: "bn4f_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2c"
+  top: "res4f_branch2c"
+  name: "scale4f_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e"
+  bottom: "res4f_branch2c"
+  top: "res4f"
+  name: "res4f"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res4f"
+  name: "res4f_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res5a_branch1"
+  name: "res5a_branch1"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  top: "res5a_branch1"
+  name: "bn5a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  top: "res5a_branch1"
+  name: "scale5a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res5a_branch2a"
+  name: "res5a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "bn5a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "scale5a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "res5a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2b"
+  name: "res5a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "bn5a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "scale5a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "res5a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2c"
+  name: "res5a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
+  name: "bn5a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
+  name: "scale5a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  bottom: "res5a_branch2c"
+  top: "res5a"
+  name: "res5a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a"
+  top: "res5a"
+  name: "res5a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a"
+  top: "res5b_branch2a"
+  name: "res5b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "bn5b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "scale5b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "res5b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2b"
+  name: "res5b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "bn5b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "scale5b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "res5b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2c"
+  name: "res5b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2c"
+  top: "res5b_branch2c"
+  name: "bn5b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2c"
+  top: "res5b_branch2c"
+  name: "scale5b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a"
+  bottom: "res5b_branch2c"
+  top: "res5b"
+  name: "res5b"
+  type: "Eltwise"
+  eltwise_param {
+  }
+}
+
+layer {
+  bottom: "res5b"
+  top: "res5b"
+  name: "res5b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b"
+  top: "res5c_branch2a"
+  name: "res5c_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "bn5c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "scale5c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "res5c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2b"
+  name: "res5c_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "bn5c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "scale5c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "res5c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2c"
+  name: "res5c_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  name: "bn5c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  name: "scale5c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b"
+  bottom: "res5c_branch2c"
+  top: "res5c"
+  name: "res5c"
+  type: "Eltwise"
+  eltwise_param {
+  }
+}
+
+layer {
+  bottom: "res5c"
+  top: "res5c"
+  name: "res5c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5c"
+  top: "pool5"
+  name: "pool5"
+  type: "Pooling"
+  pooling_param {
+    kernel_size: 7
+    stride: 1
+    pool: AVE
+  }
+}
+
+layer {
+  bottom: "pool5"
+  top: "fc1000"
+  name: "fc1000"
+  type: "InnerProduct"
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss"
+  name: "prob"
+  type: "SoftmaxWithLoss"
+  include {
+      phase: TRAIN
+  }
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss3/top-1"
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss3/top-5"
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/googlenet/bdw/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet/bdw/solver_dummydata.prototxt
deleted file mode 100644
index 87411ad22..000000000
--- a/models/intel_optimized_models/googlenet/bdw/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet/bdw/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet/bdw/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/knl/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet/knl/solver_dummydata.prototxt
deleted file mode 100644
index f45255f93..000000000
--- a/models/intel_optimized_models/googlenet/knl/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet/knl/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet/knl/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/knm/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet/knm/solver_dummydata.prototxt
deleted file mode 100644
index 7456d6748..000000000
--- a/models/intel_optimized_models/googlenet/knm/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet/knm/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet/knm/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/knm/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/knm/train_val_dummydata.prototxt
deleted file mode 100644
index 602416008..000000000
--- a/models/intel_optimized_models/googlenet/knm/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,2427 +0,0 @@
-name: "GoogleNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv1/relu_7x7"
-  type: "ReLU"
-  bottom: "conv1/7x7_s2"
-  top: "conv1/7x7_s2"
-}
-layer {
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  bottom: "conv1/7x7_s2"
-  top: "pool1/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "pool1/norm1"
-  type: "LRN"
-  bottom: "pool1/3x3_s2"
-  top: "pool1/norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool1/norm1"
-  top: "conv2/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3_reduce"
-}
-layer {
-  name: "conv2/3x3"
-  type: "Convolution"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3"
-  type: "ReLU"
-  bottom: "conv2/3x3"
-  top: "conv2/3x3"
-}
-layer {
-  name: "conv2/norm2"
-  type: "LRN"
-  bottom: "conv2/3x3"
-  top: "conv2/norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  bottom: "conv2/norm2"
-  top: "pool2/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3a/1x1"
-  top: "inception_3a/1x1"
-}
-layer {
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3_reduce"
-}
-layer {
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3a/3x3"
-  top: "inception_3a/3x3"
-}
-layer {
-  name: "inception_3a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5_reduce"
-}
-layer {
-  name: "inception_3a/5x5"
-  type: "Convolution"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3a/5x5"
-  top: "inception_3a/5x5"
-}
-layer {
-  name: "inception_3a/pool"
-  type: "Pooling"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/pool_proj"
-}
-layer {
-  name: "inception_3a/output"
-  type: "Concat"
-  bottom: "inception_3a/1x1"
-  bottom: "inception_3a/3x3"
-  bottom: "inception_3a/5x5"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/output"
-}
-layer {
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3b/1x1"
-  top: "inception_3b/1x1"
-}
-layer {
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3_reduce"
-}
-layer {
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3b/3x3"
-  top: "inception_3b/3x3"
-}
-layer {
-  name: "inception_3b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5_reduce"
-}
-layer {
-  name: "inception_3b/5x5"
-  type: "Convolution"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3b/5x5"
-  top: "inception_3b/5x5"
-}
-layer {
-  name: "inception_3b/pool"
-  type: "Pooling"
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/pool_proj"
-}
-layer {
-  name: "inception_3b/output"
-  type: "Concat"
-  bottom: "inception_3b/1x1"
-  bottom: "inception_3b/3x3"
-  bottom: "inception_3b/5x5"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/output"
-}
-layer {
-  name: "pool3/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_3b/output"
-  top: "pool3/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4a/1x1"
-  top: "inception_4a/1x1"
-}
-layer {
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3_reduce"
-}
-layer {
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 208
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4a/3x3"
-  top: "inception_4a/3x3"
-}
-layer {
-  name: "inception_4a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5_reduce"
-}
-layer {
-  name: "inception_4a/5x5"
-  type: "Convolution"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4a/5x5"
-  top: "inception_4a/5x5"
-}
-layer {
-  name: "inception_4a/pool"
-  type: "Pooling"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/pool_proj"
-}
-layer {
-  name: "inception_4a/output"
-  type: "Concat"
-  bottom: "inception_4a/1x1"
-  bottom: "inception_4a/3x3"
-  bottom: "inception_4a/5x5"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/output"
-}
-layer {
-  name: "loss1/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "loss1/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss1/conv"
-  type: "Convolution"
-  bottom: "loss1/ave_pool"
-  top: "loss1/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_conv"
-  type: "ReLU"
-  bottom: "loss1/conv"
-  top: "loss1/conv"
-}
-layer {
-  name: "loss1/fc"
-  type: "InnerProduct"
-  bottom: "loss1/conv"
-  top: "loss1/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_fc"
-  type: "ReLU"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-}
-layer {
-  name: "loss1/drop_fc"
-  type: "Dropout"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  bottom: "loss1/fc"
-  top: "loss1/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss1/top-1"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss1/top-5"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4b/1x1"
-  top: "inception_4b/1x1"
-}
-layer {
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3_reduce"
-}
-layer {
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4b/3x3"
-  top: "inception_4b/3x3"
-}
-layer {
-  name: "inception_4b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5_reduce"
-}
-layer {
-  name: "inception_4b/5x5"
-  type: "Convolution"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4b/5x5"
-  top: "inception_4b/5x5"
-}
-layer {
-  name: "inception_4b/pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/pool_proj"
-}
-layer {
-  name: "inception_4b/output"
-  type: "Concat"
-  bottom: "inception_4b/1x1"
-  bottom: "inception_4b/3x3"
-  bottom: "inception_4b/5x5"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/output"
-}
-layer {
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4c/1x1"
-  top: "inception_4c/1x1"
-}
-layer {
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3_reduce"
-}
-layer {
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4c/3x3"
-  top: "inception_4c/3x3"
-}
-layer {
-  name: "inception_4c/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5_reduce"
-}
-layer {
-  name: "inception_4c/5x5"
-  type: "Convolution"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4c/5x5"
-  top: "inception_4c/5x5"
-}
-layer {
-  name: "inception_4c/pool"
-  type: "Pooling"
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/pool_proj"
-}
-layer {
-  name: "inception_4c/output"
-  type: "Concat"
-  bottom: "inception_4c/1x1"
-  bottom: "inception_4c/3x3"
-  bottom: "inception_4c/5x5"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/output"
-}
-layer {
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4d/1x1"
-  top: "inception_4d/1x1"
-}
-layer {
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 144
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3_reduce"
-}
-layer {
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 288
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4d/3x3"
-  top: "inception_4d/3x3"
-}
-layer {
-  name: "inception_4d/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5_reduce"
-}
-layer {
-  name: "inception_4d/5x5"
-  type: "Convolution"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4d/5x5"
-  top: "inception_4d/5x5"
-}
-layer {
-  name: "inception_4d/pool"
-  type: "Pooling"
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/pool_proj"
-}
-layer {
-  name: "inception_4d/output"
-  type: "Concat"
-  bottom: "inception_4d/1x1"
-  bottom: "inception_4d/3x3"
-  bottom: "inception_4d/5x5"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/output"
-}
-layer {
-  name: "loss2/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "loss2/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss2/conv"
-  type: "Convolution"
-  bottom: "loss2/ave_pool"
-  top: "loss2/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_conv"
-  type: "ReLU"
-  bottom: "loss2/conv"
-  top: "loss2/conv"
-}
-layer {
-  name: "loss2/fc"
-  type: "InnerProduct"
-  bottom: "loss2/conv"
-  top: "loss2/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_fc"
-  type: "ReLU"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-}
-layer {
-  name: "loss2/drop_fc"
-  type: "Dropout"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  bottom: "loss2/fc"
-  top: "loss2/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss2/top-1"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss2/top-5"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4e/1x1"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4e/1x1"
-  top: "inception_4e/1x1"
-}
-layer {
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3_reduce"
-}
-layer {
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4e/3x3"
-  top: "inception_4e/3x3"
-}
-layer {
-  name: "inception_4e/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5_reduce"
-}
-layer {
-  name: "inception_4e/5x5"
-  type: "Convolution"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4e/5x5"
-  top: "inception_4e/5x5"
-}
-layer {
-  name: "inception_4e/pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4e/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/pool_proj"
-}
-layer {
-  name: "inception_4e/output"
-  type: "Concat"
-  bottom: "inception_4e/1x1"
-  bottom: "inception_4e/3x3"
-  bottom: "inception_4e/5x5"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/output"
-}
-layer {
-  name: "pool4/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_4e/output"
-  top: "pool4/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5a/1x1"
-  top: "inception_5a/1x1"
-}
-layer {
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3_reduce"
-}
-layer {
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5a/3x3"
-  top: "inception_5a/3x3"
-}
-layer {
-  name: "inception_5a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5_reduce"
-}
-layer {
-  name: "inception_5a/5x5"
-  type: "Convolution"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5a/5x5"
-  top: "inception_5a/5x5"
-}
-layer {
-  name: "inception_5a/pool"
-  type: "Pooling"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/pool_proj"
-}
-layer {
-  name: "inception_5a/output"
-  type: "Concat"
-  bottom: "inception_5a/1x1"
-  bottom: "inception_5a/3x3"
-  bottom: "inception_5a/5x5"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/output"
-}
-layer {
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5b/1x1"
-  top: "inception_5b/1x1"
-}
-layer {
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3_reduce"
-}
-layer {
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5b/3x3"
-  top: "inception_5b/3x3"
-}
-layer {
-  name: "inception_5b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5_reduce"
-}
-layer {
-  name: "inception_5b/5x5"
-  type: "Convolution"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5b/5x5"
-  top: "inception_5b/5x5"
-}
-layer {
-  name: "inception_5b/pool"
-  type: "Pooling"
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/pool_proj"
-}
-layer {
-  name: "inception_5b/output"
-  type: "Concat"
-  bottom: "inception_5b/1x1"
-  bottom: "inception_5b/3x3"
-  bottom: "inception_5b/5x5"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/output"
-}
-layer {
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  pooling_param {
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  name: "pool5/drop_7x7_s1"
-  type: "Dropout"
-  bottom: "pool5/7x7_s1"
-  top: "pool5/7x7_s1"
-  dropout_param {
-    dropout_ratio: 0.4
-  }
-}
-layer {
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss3/loss3"
-  type: "SoftmaxWithLoss"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss3"
-  loss_weight: 1
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/googlenet/skx/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet/skx/solver_dummydata.prototxt
deleted file mode 100644
index bc2d576a0..000000000
--- a/models/intel_optimized_models/googlenet/skx/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet/skx/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt
deleted file mode 100644
index 602416008..000000000
--- a/models/intel_optimized_models/googlenet/skx/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,2427 +0,0 @@
-name: "GoogleNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv1/relu_7x7"
-  type: "ReLU"
-  bottom: "conv1/7x7_s2"
-  top: "conv1/7x7_s2"
-}
-layer {
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  bottom: "conv1/7x7_s2"
-  top: "pool1/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "pool1/norm1"
-  type: "LRN"
-  bottom: "pool1/3x3_s2"
-  top: "pool1/norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool1/norm1"
-  top: "conv2/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3_reduce"
-}
-layer {
-  name: "conv2/3x3"
-  type: "Convolution"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3"
-  type: "ReLU"
-  bottom: "conv2/3x3"
-  top: "conv2/3x3"
-}
-layer {
-  name: "conv2/norm2"
-  type: "LRN"
-  bottom: "conv2/3x3"
-  top: "conv2/norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  bottom: "conv2/norm2"
-  top: "pool2/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3a/1x1"
-  top: "inception_3a/1x1"
-}
-layer {
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3_reduce"
-}
-layer {
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3a/3x3"
-  top: "inception_3a/3x3"
-}
-layer {
-  name: "inception_3a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5_reduce"
-}
-layer {
-  name: "inception_3a/5x5"
-  type: "Convolution"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3a/5x5"
-  top: "inception_3a/5x5"
-}
-layer {
-  name: "inception_3a/pool"
-  type: "Pooling"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/pool_proj"
-}
-layer {
-  name: "inception_3a/output"
-  type: "Concat"
-  bottom: "inception_3a/1x1"
-  bottom: "inception_3a/3x3"
-  bottom: "inception_3a/5x5"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/output"
-}
-layer {
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3b/1x1"
-  top: "inception_3b/1x1"
-}
-layer {
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3_reduce"
-}
-layer {
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3b/3x3"
-  top: "inception_3b/3x3"
-}
-layer {
-  name: "inception_3b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5_reduce"
-}
-layer {
-  name: "inception_3b/5x5"
-  type: "Convolution"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3b/5x5"
-  top: "inception_3b/5x5"
-}
-layer {
-  name: "inception_3b/pool"
-  type: "Pooling"
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/pool_proj"
-}
-layer {
-  name: "inception_3b/output"
-  type: "Concat"
-  bottom: "inception_3b/1x1"
-  bottom: "inception_3b/3x3"
-  bottom: "inception_3b/5x5"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/output"
-}
-layer {
-  name: "pool3/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_3b/output"
-  top: "pool3/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4a/1x1"
-  top: "inception_4a/1x1"
-}
-layer {
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3_reduce"
-}
-layer {
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 208
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4a/3x3"
-  top: "inception_4a/3x3"
-}
-layer {
-  name: "inception_4a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5_reduce"
-}
-layer {
-  name: "inception_4a/5x5"
-  type: "Convolution"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4a/5x5"
-  top: "inception_4a/5x5"
-}
-layer {
-  name: "inception_4a/pool"
-  type: "Pooling"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/pool_proj"
-}
-layer {
-  name: "inception_4a/output"
-  type: "Concat"
-  bottom: "inception_4a/1x1"
-  bottom: "inception_4a/3x3"
-  bottom: "inception_4a/5x5"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/output"
-}
-layer {
-  name: "loss1/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "loss1/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss1/conv"
-  type: "Convolution"
-  bottom: "loss1/ave_pool"
-  top: "loss1/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_conv"
-  type: "ReLU"
-  bottom: "loss1/conv"
-  top: "loss1/conv"
-}
-layer {
-  name: "loss1/fc"
-  type: "InnerProduct"
-  bottom: "loss1/conv"
-  top: "loss1/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_fc"
-  type: "ReLU"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-}
-layer {
-  name: "loss1/drop_fc"
-  type: "Dropout"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  bottom: "loss1/fc"
-  top: "loss1/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss1/top-1"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss1/top-5"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4b/1x1"
-  top: "inception_4b/1x1"
-}
-layer {
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3_reduce"
-}
-layer {
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4b/3x3"
-  top: "inception_4b/3x3"
-}
-layer {
-  name: "inception_4b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5_reduce"
-}
-layer {
-  name: "inception_4b/5x5"
-  type: "Convolution"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4b/5x5"
-  top: "inception_4b/5x5"
-}
-layer {
-  name: "inception_4b/pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/pool_proj"
-}
-layer {
-  name: "inception_4b/output"
-  type: "Concat"
-  bottom: "inception_4b/1x1"
-  bottom: "inception_4b/3x3"
-  bottom: "inception_4b/5x5"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/output"
-}
-layer {
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4c/1x1"
-  top: "inception_4c/1x1"
-}
-layer {
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3_reduce"
-}
-layer {
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4c/3x3"
-  top: "inception_4c/3x3"
-}
-layer {
-  name: "inception_4c/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5_reduce"
-}
-layer {
-  name: "inception_4c/5x5"
-  type: "Convolution"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4c/5x5"
-  top: "inception_4c/5x5"
-}
-layer {
-  name: "inception_4c/pool"
-  type: "Pooling"
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/pool_proj"
-}
-layer {
-  name: "inception_4c/output"
-  type: "Concat"
-  bottom: "inception_4c/1x1"
-  bottom: "inception_4c/3x3"
-  bottom: "inception_4c/5x5"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/output"
-}
-layer {
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4d/1x1"
-  top: "inception_4d/1x1"
-}
-layer {
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 144
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3_reduce"
-}
-layer {
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 288
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4d/3x3"
-  top: "inception_4d/3x3"
-}
-layer {
-  name: "inception_4d/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5_reduce"
-}
-layer {
-  name: "inception_4d/5x5"
-  type: "Convolution"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4d/5x5"
-  top: "inception_4d/5x5"
-}
-layer {
-  name: "inception_4d/pool"
-  type: "Pooling"
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/pool_proj"
-}
-layer {
-  name: "inception_4d/output"
-  type: "Concat"
-  bottom: "inception_4d/1x1"
-  bottom: "inception_4d/3x3"
-  bottom: "inception_4d/5x5"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/output"
-}
-layer {
-  name: "loss2/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "loss2/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss2/conv"
-  type: "Convolution"
-  bottom: "loss2/ave_pool"
-  top: "loss2/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_conv"
-  type: "ReLU"
-  bottom: "loss2/conv"
-  top: "loss2/conv"
-}
-layer {
-  name: "loss2/fc"
-  type: "InnerProduct"
-  bottom: "loss2/conv"
-  top: "loss2/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_fc"
-  type: "ReLU"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-}
-layer {
-  name: "loss2/drop_fc"
-  type: "Dropout"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  bottom: "loss2/fc"
-  top: "loss2/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss2/top-1"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss2/top-5"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4e/1x1"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4e/1x1"
-  top: "inception_4e/1x1"
-}
-layer {
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3_reduce"
-}
-layer {
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4e/3x3"
-  top: "inception_4e/3x3"
-}
-layer {
-  name: "inception_4e/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5_reduce"
-}
-layer {
-  name: "inception_4e/5x5"
-  type: "Convolution"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4e/5x5"
-  top: "inception_4e/5x5"
-}
-layer {
-  name: "inception_4e/pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4e/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/pool_proj"
-}
-layer {
-  name: "inception_4e/output"
-  type: "Concat"
-  bottom: "inception_4e/1x1"
-  bottom: "inception_4e/3x3"
-  bottom: "inception_4e/5x5"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/output"
-}
-layer {
-  name: "pool4/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_4e/output"
-  top: "pool4/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5a/1x1"
-  top: "inception_5a/1x1"
-}
-layer {
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3_reduce"
-}
-layer {
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5a/3x3"
-  top: "inception_5a/3x3"
-}
-layer {
-  name: "inception_5a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5_reduce"
-}
-layer {
-  name: "inception_5a/5x5"
-  type: "Convolution"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5a/5x5"
-  top: "inception_5a/5x5"
-}
-layer {
-  name: "inception_5a/pool"
-  type: "Pooling"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/pool_proj"
-}
-layer {
-  name: "inception_5a/output"
-  type: "Concat"
-  bottom: "inception_5a/1x1"
-  bottom: "inception_5a/3x3"
-  bottom: "inception_5a/5x5"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/output"
-}
-layer {
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5b/1x1"
-  top: "inception_5b/1x1"
-}
-layer {
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3_reduce"
-}
-layer {
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5b/3x3"
-  top: "inception_5b/3x3"
-}
-layer {
-  name: "inception_5b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5_reduce"
-}
-layer {
-  name: "inception_5b/5x5"
-  type: "Convolution"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5b/5x5"
-  top: "inception_5b/5x5"
-}
-layer {
-  name: "inception_5b/pool"
-  type: "Pooling"
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/pool_proj"
-}
-layer {
-  name: "inception_5b/output"
-  type: "Concat"
-  bottom: "inception_5b/1x1"
-  bottom: "inception_5b/3x3"
-  bottom: "inception_5b/5x5"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/output"
-}
-layer {
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  pooling_param {
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  name: "pool5/drop_7x7_s1"
-  type: "Dropout"
-  bottom: "pool5/7x7_s1"
-  top: "pool5/7x7_s1"
-  dropout_param {
-    dropout_ratio: 0.4
-  }
-}
-layer {
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss3/loss3"
-  type: "SoftmaxWithLoss"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss3"
-  loss_weight: 1
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/googlenet/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet/train_val_dummydata.prototxt
deleted file mode 100644
index bf411dfe7..000000000
--- a/models/intel_optimized_models/googlenet/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,2427 +0,0 @@
-name: "GoogleNet"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 64 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv1/relu_7x7"
-  type: "ReLU"
-  bottom: "conv1/7x7_s2"
-  top: "conv1/7x7_s2"
-}
-layer {
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  bottom: "conv1/7x7_s2"
-  top: "pool1/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "pool1/norm1"
-  type: "LRN"
-  bottom: "pool1/3x3_s2"
-  top: "pool1/norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool1/norm1"
-  top: "conv2/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3_reduce"
-}
-layer {
-  name: "conv2/3x3"
-  type: "Convolution"
-  bottom: "conv2/3x3_reduce"
-  top: "conv2/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "conv2/relu_3x3"
-  type: "ReLU"
-  bottom: "conv2/3x3"
-  top: "conv2/3x3"
-}
-layer {
-  name: "conv2/norm2"
-  type: "LRN"
-  bottom: "conv2/3x3"
-  top: "conv2/norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layer {
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  bottom: "conv2/norm2"
-  top: "pool2/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3a/1x1"
-  top: "inception_3a/1x1"
-}
-layer {
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3_reduce"
-}
-layer {
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  bottom: "inception_3a/3x3_reduce"
-  top: "inception_3a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3a/3x3"
-  top: "inception_3a/3x3"
-}
-layer {
-  name: "inception_3a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5_reduce"
-}
-layer {
-  name: "inception_3a/5x5"
-  type: "Convolution"
-  bottom: "inception_3a/5x5_reduce"
-  top: "inception_3a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3a/5x5"
-  top: "inception_3a/5x5"
-}
-layer {
-  name: "inception_3a/pool"
-  type: "Pooling"
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/pool_proj"
-}
-layer {
-  name: "inception_3a/output"
-  type: "Concat"
-  bottom: "inception_3a/1x1"
-  bottom: "inception_3a/3x3"
-  bottom: "inception_3a/5x5"
-  bottom: "inception_3a/pool_proj"
-  top: "inception_3a/output"
-}
-layer {
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_3b/1x1"
-  top: "inception_3b/1x1"
-}
-layer {
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3_reduce"
-}
-layer {
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  bottom: "inception_3b/3x3_reduce"
-  top: "inception_3b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_3b/3x3"
-  top: "inception_3b/3x3"
-}
-layer {
-  name: "inception_3b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_3a/output"
-  top: "inception_3b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5_reduce"
-}
-layer {
-  name: "inception_3b/5x5"
-  type: "Convolution"
-  bottom: "inception_3b/5x5_reduce"
-  top: "inception_3b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_3b/5x5"
-  top: "inception_3b/5x5"
-}
-layer {
-  name: "inception_3b/pool"
-  type: "Pooling"
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_3b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/pool_proj"
-}
-layer {
-  name: "inception_3b/output"
-  type: "Concat"
-  bottom: "inception_3b/1x1"
-  bottom: "inception_3b/3x3"
-  bottom: "inception_3b/5x5"
-  bottom: "inception_3b/pool_proj"
-  top: "inception_3b/output"
-}
-layer {
-  name: "pool3/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_3b/output"
-  top: "pool3/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4a/1x1"
-  top: "inception_4a/1x1"
-}
-layer {
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 96
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3_reduce"
-}
-layer {
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  bottom: "inception_4a/3x3_reduce"
-  top: "inception_4a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 208
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4a/3x3"
-  top: "inception_4a/3x3"
-}
-layer {
-  name: "inception_4a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 16
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5_reduce"
-}
-layer {
-  name: "inception_4a/5x5"
-  type: "Convolution"
-  bottom: "inception_4a/5x5_reduce"
-  top: "inception_4a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4a/5x5"
-  top: "inception_4a/5x5"
-}
-layer {
-  name: "inception_4a/pool"
-  type: "Pooling"
-  bottom: "pool3/3x3_s2"
-  top: "inception_4a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/pool_proj"
-}
-layer {
-  name: "inception_4a/output"
-  type: "Concat"
-  bottom: "inception_4a/1x1"
-  bottom: "inception_4a/3x3"
-  bottom: "inception_4a/5x5"
-  bottom: "inception_4a/pool_proj"
-  top: "inception_4a/output"
-}
-layer {
-  name: "loss1/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "loss1/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss1/conv"
-  type: "Convolution"
-  bottom: "loss1/ave_pool"
-  top: "loss1/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_conv"
-  type: "ReLU"
-  bottom: "loss1/conv"
-  top: "loss1/conv"
-}
-layer {
-  name: "loss1/fc"
-  type: "InnerProduct"
-  bottom: "loss1/conv"
-  top: "loss1/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss1/relu_fc"
-  type: "ReLU"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-}
-layer {
-  name: "loss1/drop_fc"
-  type: "Dropout"
-  bottom: "loss1/fc"
-  top: "loss1/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  bottom: "loss1/fc"
-  top: "loss1/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss1/top-1"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss1/top-5"
-  type: "Accuracy"
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4b/1x1"
-  top: "inception_4b/1x1"
-}
-layer {
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3_reduce"
-}
-layer {
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  bottom: "inception_4b/3x3_reduce"
-  top: "inception_4b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4b/3x3"
-  top: "inception_4b/3x3"
-}
-layer {
-  name: "inception_4b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4a/output"
-  top: "inception_4b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5_reduce"
-}
-layer {
-  name: "inception_4b/5x5"
-  type: "Convolution"
-  bottom: "inception_4b/5x5_reduce"
-  top: "inception_4b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4b/5x5"
-  top: "inception_4b/5x5"
-}
-layer {
-  name: "inception_4b/pool"
-  type: "Pooling"
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/pool_proj"
-}
-layer {
-  name: "inception_4b/output"
-  type: "Concat"
-  bottom: "inception_4b/1x1"
-  bottom: "inception_4b/3x3"
-  bottom: "inception_4b/5x5"
-  bottom: "inception_4b/pool_proj"
-  top: "inception_4b/output"
-}
-layer {
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4c/1x1"
-  top: "inception_4c/1x1"
-}
-layer {
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3_reduce"
-}
-layer {
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  bottom: "inception_4c/3x3_reduce"
-  top: "inception_4c/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4c/3x3"
-  top: "inception_4c/3x3"
-}
-layer {
-  name: "inception_4c/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4b/output"
-  top: "inception_4c/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 24
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5_reduce"
-}
-layer {
-  name: "inception_4c/5x5"
-  type: "Convolution"
-  bottom: "inception_4c/5x5_reduce"
-  top: "inception_4c/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4c/5x5"
-  top: "inception_4c/5x5"
-}
-layer {
-  name: "inception_4c/pool"
-  type: "Pooling"
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4c/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/pool_proj"
-}
-layer {
-  name: "inception_4c/output"
-  type: "Concat"
-  bottom: "inception_4c/1x1"
-  bottom: "inception_4c/3x3"
-  bottom: "inception_4c/5x5"
-  bottom: "inception_4c/pool_proj"
-  top: "inception_4c/output"
-}
-layer {
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 112
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4d/1x1"
-  top: "inception_4d/1x1"
-}
-layer {
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 144
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3_reduce"
-}
-layer {
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  bottom: "inception_4d/3x3_reduce"
-  top: "inception_4d/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 288
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4d/3x3"
-  top: "inception_4d/3x3"
-}
-layer {
-  name: "inception_4d/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4c/output"
-  top: "inception_4d/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5_reduce"
-}
-layer {
-  name: "inception_4d/5x5"
-  type: "Convolution"
-  bottom: "inception_4d/5x5_reduce"
-  top: "inception_4d/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4d/5x5"
-  top: "inception_4d/5x5"
-}
-layer {
-  name: "inception_4d/pool"
-  type: "Pooling"
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4d/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/pool_proj"
-}
-layer {
-  name: "inception_4d/output"
-  type: "Concat"
-  bottom: "inception_4d/1x1"
-  bottom: "inception_4d/3x3"
-  bottom: "inception_4d/5x5"
-  bottom: "inception_4d/pool_proj"
-  top: "inception_4d/output"
-}
-layer {
-  name: "loss2/ave_pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "loss2/ave_pool"
-  pooling_param {
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
-  name: "loss2/conv"
-  type: "Convolution"
-  bottom: "loss2/ave_pool"
-  top: "loss2/conv"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_conv"
-  type: "ReLU"
-  bottom: "loss2/conv"
-  top: "loss2/conv"
-}
-layer {
-  name: "loss2/fc"
-  type: "InnerProduct"
-  bottom: "loss2/conv"
-  top: "loss2/fc"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "loss2/relu_fc"
-  type: "ReLU"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-}
-layer {
-  name: "loss2/drop_fc"
-  type: "Dropout"
-  bottom: "loss2/fc"
-  top: "loss2/fc"
-  dropout_param {
-    dropout_ratio: 0.7
-  }
-}
-layer {
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  bottom: "loss2/fc"
-  top: "loss2/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss1"
-  loss_weight: 0.3
-}
-layer {
-  name: "loss2/top-1"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss2/top-5"
-  type: "Accuracy"
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
-layer {
-  name: "inception_4e/1x1"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_4e/1x1"
-  top: "inception_4e/1x1"
-}
-layer {
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3_reduce"
-}
-layer {
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  bottom: "inception_4e/3x3_reduce"
-  top: "inception_4e/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_4e/3x3"
-  top: "inception_4e/3x3"
-}
-layer {
-  name: "inception_4e/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_4d/output"
-  top: "inception_4e/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5_reduce"
-}
-layer {
-  name: "inception_4e/5x5"
-  type: "Convolution"
-  bottom: "inception_4e/5x5_reduce"
-  top: "inception_4e/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_4e/5x5"
-  top: "inception_4e/5x5"
-}
-layer {
-  name: "inception_4e/pool"
-  type: "Pooling"
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_4e/pool_proj"
-  type: "Convolution"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_4e/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/pool_proj"
-}
-layer {
-  name: "inception_4e/output"
-  type: "Concat"
-  bottom: "inception_4e/1x1"
-  bottom: "inception_4e/3x3"
-  bottom: "inception_4e/5x5"
-  bottom: "inception_4e/pool_proj"
-  top: "inception_4e/output"
-}
-layer {
-  name: "pool4/3x3_s2"
-  type: "Pooling"
-  bottom: "inception_4e/output"
-  top: "pool4/3x3_s2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5a/1x1"
-  top: "inception_5a/1x1"
-}
-layer {
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 160
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3_reduce"
-}
-layer {
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  bottom: "inception_5a/3x3_reduce"
-  top: "inception_5a/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5a/3x3"
-  top: "inception_5a/3x3"
-}
-layer {
-  name: "inception_5a/5x5_reduce"
-  type: "Convolution"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 32
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5_reduce"
-}
-layer {
-  name: "inception_5a/5x5"
-  type: "Convolution"
-  bottom: "inception_5a/5x5_reduce"
-  top: "inception_5a/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5a/5x5"
-  top: "inception_5a/5x5"
-}
-layer {
-  name: "inception_5a/pool"
-  type: "Pooling"
-  bottom: "pool4/3x3_s2"
-  top: "inception_5a/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5a/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/pool_proj"
-}
-layer {
-  name: "inception_5a/output"
-  type: "Concat"
-  bottom: "inception_5a/1x1"
-  bottom: "inception_5a/3x3"
-  bottom: "inception_5a/5x5"
-  bottom: "inception_5a/pool_proj"
-  top: "inception_5a/output"
-}
-layer {
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_1x1"
-  type: "ReLU"
-  bottom: "inception_5b/1x1"
-  top: "inception_5b/1x1"
-}
-layer {
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 192
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3_reduce"
-}
-layer {
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  bottom: "inception_5b/3x3_reduce"
-  top: "inception_5b/3x3"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_3x3"
-  type: "ReLU"
-  bottom: "inception_5b/3x3"
-  top: "inception_5b/3x3"
-}
-layer {
-  name: "inception_5b/5x5_reduce"
-  type: "Convolution"
-  bottom: "inception_5a/output"
-  top: "inception_5b/5x5_reduce"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 48
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5_reduce"
-  type: "ReLU"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5_reduce"
-}
-layer {
-  name: "inception_5b/5x5"
-  type: "Convolution"
-  bottom: "inception_5b/5x5_reduce"
-  top: "inception_5b/5x5"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    pad: 2
-    kernel_size: 5
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_5x5"
-  type: "ReLU"
-  bottom: "inception_5b/5x5"
-  top: "inception_5b/5x5"
-}
-layer {
-  name: "inception_5b/pool"
-  type: "Pooling"
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.1
-    }
-  }
-}
-layer {
-  name: "inception_5b/relu_pool_proj"
-  type: "ReLU"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/pool_proj"
-}
-layer {
-  name: "inception_5b/output"
-  type: "Concat"
-  bottom: "inception_5b/1x1"
-  bottom: "inception_5b/3x3"
-  bottom: "inception_5b/5x5"
-  bottom: "inception_5b/pool_proj"
-  top: "inception_5b/output"
-}
-layer {
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  pooling_param {
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  name: "pool5/drop_7x7_s1"
-  type: "Dropout"
-  bottom: "pool5/7x7_s1"
-  top: "pool5/7x7_s1"
-  dropout_param {
-    dropout_ratio: 0.4
-  }
-}
-layer {
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  name: "loss3/loss3"
-  type: "SoftmaxWithLoss"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss3"
-  loss_weight: 1
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/googlenet_v2/bdw/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/bdw/solver_dummydata.prototxt
deleted file mode 100644
index a823d4fec..000000000
--- a/models/intel_optimized_models/googlenet_v2/bdw/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet_v2/bdw/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet_v2/bdw/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/knl/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/knl/solver_dummydata.prototxt
deleted file mode 100644
index 3009ea077..000000000
--- a/models/intel_optimized_models/googlenet_v2/knl/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet_v2/knl/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet_v2/knl/googlenet_v2_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/knm/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/knm/solver_dummydata.prototxt
deleted file mode 100644
index 9a64c3e00..000000000
--- a/models/intel_optimized_models/googlenet_v2/knm/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet_v2/knm/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet_v2/knm/googlenet_v2_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/knm/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/knm/train_val_dummydata.prototxt
deleted file mode 100644
index 63ca66d97..000000000
--- a/models/intel_optimized_models/googlenet_v2/knm/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,4034 +0,0 @@
-# Inception Network (GoogLeNet Batch Normalization Network)
-name: "InceptionNetwork"
-### Training Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-### Validation Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 192 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 192 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2"
-  name: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool1/3x3_s2"
-  top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3"
-  name: "conv2/3x3/bn"
-  top: "conv2/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/1x1"
-  name: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3"
-  name: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn/sc"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn/sc"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 32
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  bottom: "inception_3a/3x3/bn/sc"
-  bottom: "inception_3a/double3x3b/bn/sc"
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/1x1"
-  name: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3"
-  name: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn/sc"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn/sc"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  bottom: "inception_3b/3x3/bn/sc"
-  bottom: "inception_3b/double3x3b/bn/sc"
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3"
-  name: "inception_3c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3"
-  name: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn/sc"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/output"
-  top: "inception_3c/pool"
-  name: "inception_3c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  bottom: "inception_3c/double3x3b/bn/sc"
-  bottom: "inception_3c/pool"
-  top: "inception_3c/output"
-  name: "inception_3c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "pool3/5x5_s3"
-  name: "pool3/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool3/5x5_s3"
-  top: "loss1/conv"
-  name: "loss1/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/conv"
-  name: "loss1/conv/bn"
-  top: "loss1/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/fc"
-  name: "loss1/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/fc"
-  name: "loss1/fc/bn"
-  top: "loss1/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/classifier"
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss"
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss1/classifier"
-  top: "loss1/prob"
-  name: "loss1/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-1"
-  name: "loss1/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-5"
-  name: "loss1/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/1x1"
-  name: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3"
-  name: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn/sc"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn/sc"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  bottom: "inception_4a/3x3/bn/sc"
-  bottom: "inception_4a/double3x3b/bn/sc"
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/1x1"
-  name: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3"
-  name: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn/sc"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn/sc"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  bottom: "inception_4b/3x3/bn/sc"
-  bottom: "inception_4b/double3x3b/bn/sc"
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/1x1"
-  name: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3"
-  name: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn/sc"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn/sc"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  bottom: "inception_4c/3x3/bn/sc"
-  bottom: "inception_4c/double3x3b/bn/sc"
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/1x1"
-  name: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3"
-  name: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn/sc"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn/sc"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  bottom: "inception_4d/3x3/bn/sc"
-  bottom: "inception_4d/double3x3b/bn/sc"
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3"
-  name: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn/sc"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  bottom: "inception_4e/double3x3b/bn/sc"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "pool4/5x5_s3"
-  name: "pool4/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool4/5x5_s3"
-  top: "loss2/conv"
-  name: "loss2/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/conv"
-  name: "loss2/conv/bn"
-  top: "loss2/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/fc"
-  name: "loss2/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/fc"
-  name: "loss2/fc/bn"
-  top: "loss2/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/classifier"
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss"
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss2/classifier"
-  top: "loss2/prob"
-  name: "loss2/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-1"
-  name: "loss2/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-5"
-  name: "loss2/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/1x1"
-  name: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3"
-  name: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn/sc"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn/sc"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  bottom: "inception_5a/3x3/bn/sc"
-  bottom: "inception_5a/double3x3b/bn/sc"
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/1x1"
-  name: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3"
-  name: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn/sc"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn/sc"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  bottom: "inception_5b/3x3/bn/sc"
-  bottom: "inception_5b/double3x3b/bn/sc"
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss"
-  name: "loss3/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 1
-}
-layer {
-  bottom: "loss3/classifier"
-  top: "loss3/prob"
-  name: "loss3/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-1"
-  name: "loss3/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-5"
-  name: "loss3/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
diff --git a/models/intel_optimized_models/googlenet_v2/skx/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/skx/solver_dummydata.prototxt
deleted file mode 100644
index b4f4578de..000000000
--- a/models/intel_optimized_models/googlenet_v2/skx/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet_v2/skx/googlenet_v2_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt
deleted file mode 100644
index 63ca66d97..000000000
--- a/models/intel_optimized_models/googlenet_v2/skx/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,4034 +0,0 @@
-# Inception Network (GoogLeNet Batch Normalization Network)
-name: "InceptionNetwork"
-### Training Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 128 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 128 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-### Validation Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 192 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 192 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2"
-  name: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool1/3x3_s2"
-  top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3"
-  name: "conv2/3x3/bn"
-  top: "conv2/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/1x1"
-  name: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3"
-  name: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn/sc"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn/sc"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 32
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  bottom: "inception_3a/3x3/bn/sc"
-  bottom: "inception_3a/double3x3b/bn/sc"
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/1x1"
-  name: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3"
-  name: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn/sc"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn/sc"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  bottom: "inception_3b/3x3/bn/sc"
-  bottom: "inception_3b/double3x3b/bn/sc"
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3"
-  name: "inception_3c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3"
-  name: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn/sc"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/output"
-  top: "inception_3c/pool"
-  name: "inception_3c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  bottom: "inception_3c/double3x3b/bn/sc"
-  bottom: "inception_3c/pool"
-  top: "inception_3c/output"
-  name: "inception_3c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "pool3/5x5_s3"
-  name: "pool3/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool3/5x5_s3"
-  top: "loss1/conv"
-  name: "loss1/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/conv"
-  name: "loss1/conv/bn"
-  top: "loss1/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/fc"
-  name: "loss1/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/fc"
-  name: "loss1/fc/bn"
-  top: "loss1/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/classifier"
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss"
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss1/classifier"
-  top: "loss1/prob"
-  name: "loss1/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-1"
-  name: "loss1/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-5"
-  name: "loss1/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/1x1"
-  name: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3"
-  name: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn/sc"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn/sc"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  bottom: "inception_4a/3x3/bn/sc"
-  bottom: "inception_4a/double3x3b/bn/sc"
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/1x1"
-  name: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3"
-  name: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn/sc"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn/sc"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  bottom: "inception_4b/3x3/bn/sc"
-  bottom: "inception_4b/double3x3b/bn/sc"
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/1x1"
-  name: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3"
-  name: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn/sc"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn/sc"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  bottom: "inception_4c/3x3/bn/sc"
-  bottom: "inception_4c/double3x3b/bn/sc"
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/1x1"
-  name: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3"
-  name: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn/sc"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn/sc"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  bottom: "inception_4d/3x3/bn/sc"
-  bottom: "inception_4d/double3x3b/bn/sc"
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3"
-  name: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn/sc"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  bottom: "inception_4e/double3x3b/bn/sc"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "pool4/5x5_s3"
-  name: "pool4/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool4/5x5_s3"
-  top: "loss2/conv"
-  name: "loss2/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/conv"
-  name: "loss2/conv/bn"
-  top: "loss2/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/fc"
-  name: "loss2/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/fc"
-  name: "loss2/fc/bn"
-  top: "loss2/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/classifier"
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss"
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss2/classifier"
-  top: "loss2/prob"
-  name: "loss2/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-1"
-  name: "loss2/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-5"
-  name: "loss2/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/1x1"
-  name: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3"
-  name: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn/sc"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn/sc"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  bottom: "inception_5a/3x3/bn/sc"
-  bottom: "inception_5a/double3x3b/bn/sc"
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/1x1"
-  name: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3"
-  name: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn/sc"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn/sc"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  bottom: "inception_5b/3x3/bn/sc"
-  bottom: "inception_5b/double3x3b/bn/sc"
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss"
-  name: "loss3/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 1
-}
-layer {
-  bottom: "loss3/classifier"
-  top: "loss3/prob"
-  name: "loss3/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-1"
-  name: "loss3/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-5"
-  name: "loss3/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
diff --git a/models/intel_optimized_models/googlenet_v2/solver_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/solver_dummydata.prototxt
deleted file mode 100644
index 9162b472c..000000000
--- a/models/intel_optimized_models/googlenet_v2/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/googlenet_v2/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/googlenet_v2/googlenet_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/googlenet_v2/train_val_dummydata.prototxt b/models/intel_optimized_models/googlenet_v2/train_val_dummydata.prototxt
deleted file mode 100644
index 2908c3841..000000000
--- a/models/intel_optimized_models/googlenet_v2/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,4034 +0,0 @@
-# Inception Network (GoogLeNet Batch Normalization Network)
-name: "InceptionNetwork"
-### Training Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-        type: "constant"
-        value: 0.01
-    }
-    shape: { dim: 96 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 96 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-### Validation Set
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2"
-  name: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "conv1/7x7_s2/bn/sc"
-  name: "conv1/7x7_s2/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv1/7x7_s2/bn/sc"
-  top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool1/3x3_s2"
-  top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3_reduce/bn/sc"
-  name: "conv2/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "conv2/3x3_reduce/bn/sc"
-  top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "conv2/3x3"
-  name: "conv2/3x3/bn"
-  top: "conv2/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "conv2/3x3/bn/sc"
-  name: "conv2/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "conv2/3x3/bn/sc"
-  top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/1x1"
-  name: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  top: "inception_3a/1x1/bn/sc"
-  name: "inception_3a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3_reduce/bn/sc"
-  name: "inception_3a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/3x3_reduce/bn/sc"
-  top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/3x3"
-  name: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/3x3/bn/sc"
-  top: "inception_3a/3x3/bn/sc"
-  name: "inception_3a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "pool2/3x3_s2"
-  top: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce"
-  name: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3_reduce/bn/sc"
-  name: "inception_3a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3_reduce/bn/sc"
-  top: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a"
-  name: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3a/bn/sc"
-  name: "inception_3a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/double3x3a/bn/sc"
-  top: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b"
-  name: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/double3x3b/bn/sc"
-  top: "inception_3a/double3x3b/bn/sc"
-  name: "inception_3a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "pool2/3x3_s2"
-  top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3a/pool"
-  top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 32
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/pool_proj/bn/sc"
-  name: "inception_3a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/1x1/bn/sc"
-  bottom: "inception_3a/3x3/bn/sc"
-  bottom: "inception_3a/double3x3b/bn/sc"
-  bottom: "inception_3a/pool_proj/bn/sc"
-  top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/1x1"
-  name: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  top: "inception_3b/1x1/bn/sc"
-  name: "inception_3b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3_reduce/bn/sc"
-  name: "inception_3b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/3x3_reduce/bn/sc"
-  top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/3x3"
-  name: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/3x3/bn/sc"
-  top: "inception_3b/3x3/bn/sc"
-  name: "inception_3b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3a/output"
-  top: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce"
-  name: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3_reduce/bn/sc"
-  name: "inception_3b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3_reduce/bn/sc"
-  top: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a"
-  name: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3a/bn/sc"
-  name: "inception_3b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/double3x3a/bn/sc"
-  top: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b"
-  name: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/double3x3b/bn/sc"
-  top: "inception_3b/double3x3b/bn/sc"
-  name: "inception_3b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3a/output"
-  top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_3b/pool"
-  top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/pool_proj/bn/sc"
-  name: "inception_3b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/1x1/bn/sc"
-  bottom: "inception_3b/3x3/bn/sc"
-  bottom: "inception_3b/double3x3b/bn/sc"
-  bottom: "inception_3b/pool_proj/bn/sc"
-  top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce"
-  name: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3_reduce/bn/sc"
-  name: "inception_3c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/3x3_reduce/bn/sc"
-  top: "inception_3c/3x3"
-  name: "inception_3c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/3x3"
-  name: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  top: "inception_3c/3x3/bn/sc"
-  name: "inception_3c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3b/output"
-  top: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce"
-  name: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3_reduce/bn/sc"
-  name: "inception_3c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3_reduce/bn/sc"
-  top: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a"
-  name: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3a/bn/sc"
-  name: "inception_3c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/double3x3a/bn/sc"
-  top: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b"
-  name: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_3c/double3x3b/bn/sc"
-  top: "inception_3c/double3x3b/bn/sc"
-  name: "inception_3c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3b/output"
-  top: "inception_3c/pool"
-  name: "inception_3c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_3c/3x3/bn/sc"
-  bottom: "inception_3c/double3x3b/bn/sc"
-  bottom: "inception_3c/pool"
-  top: "inception_3c/output"
-  name: "inception_3c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "pool3/5x5_s3"
-  name: "pool3/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool3/5x5_s3"
-  top: "loss1/conv"
-  name: "loss1/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/conv"
-  name: "loss1/conv/bn"
-  top: "loss1/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/conv/bn/sc"
-  name: "loss1/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/conv/bn/sc"
-  top: "loss1/fc"
-  name: "loss1/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss1/fc"
-  name: "loss1/fc/bn"
-  top: "loss1/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/fc/bn/sc"
-  name: "loss1/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss1/fc/bn/sc"
-  top: "loss1/classifier"
-  name: "loss1/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss1/classifier"
-  bottom: "label"
-  top: "loss1/loss"
-  name: "loss1/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss1/classifier"
-  top: "loss1/prob"
-  name: "loss1/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-1"
-  name: "loss1/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss1/prob"
-  bottom: "label"
-  top: "loss1/top-5"
-  name: "loss1/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/1x1"
-  name: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  top: "inception_4a/1x1/bn/sc"
-  name: "inception_4a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 64
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3_reduce/bn/sc"
-  name: "inception_4a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/3x3_reduce/bn/sc"
-  top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/3x3"
-  name: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/3x3/bn/sc"
-  top: "inception_4a/3x3/bn/sc"
-  name: "inception_4a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_3c/output"
-  top: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce"
-  name: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3_reduce/bn/sc"
-  name: "inception_4a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3_reduce/bn/sc"
-  top: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a"
-  name: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3a/bn/sc"
-  name: "inception_4a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/double3x3a/bn/sc"
-  top: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b"
-  name: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/double3x3b/bn/sc"
-  top: "inception_4a/double3x3b/bn/sc"
-  name: "inception_4a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_3c/output"
-  top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4a/pool"
-  top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/pool_proj/bn/sc"
-  name: "inception_4a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/1x1/bn/sc"
-  bottom: "inception_4a/3x3/bn/sc"
-  bottom: "inception_4a/double3x3b/bn/sc"
-  bottom: "inception_4a/pool_proj/bn/sc"
-  top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/1x1"
-  name: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  top: "inception_4b/1x1/bn/sc"
-  name: "inception_4b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3_reduce/bn/sc"
-  name: "inception_4b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/3x3_reduce/bn/sc"
-  top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/3x3"
-  name: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/3x3/bn/sc"
-  top: "inception_4b/3x3/bn/sc"
-  name: "inception_4b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4a/output"
-  top: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce"
-  name: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3_reduce/bn/sc"
-  name: "inception_4b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3_reduce/bn/sc"
-  top: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a"
-  name: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3a/bn/sc"
-  name: "inception_4b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/double3x3a/bn/sc"
-  top: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b"
-  name: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/double3x3b/bn/sc"
-  top: "inception_4b/double3x3b/bn/sc"
-  name: "inception_4b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4a/output"
-  top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4b/pool"
-  top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/pool_proj/bn/sc"
-  name: "inception_4b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/1x1/bn/sc"
-  bottom: "inception_4b/3x3/bn/sc"
-  bottom: "inception_4b/double3x3b/bn/sc"
-  bottom: "inception_4b/pool_proj/bn/sc"
-  top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/1x1"
-  name: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  top: "inception_4c/1x1/bn/sc"
-  name: "inception_4c/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3_reduce/bn/sc"
-  name: "inception_4c/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/3x3_reduce/bn/sc"
-  top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/3x3"
-  name: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/3x3/bn/sc"
-  top: "inception_4c/3x3/bn/sc"
-  name: "inception_4c/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4b/output"
-  top: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce"
-  name: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3_reduce/bn/sc"
-  name: "inception_4c/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3_reduce/bn/sc"
-  top: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a"
-  name: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3a/bn/sc"
-  name: "inception_4c/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/double3x3a/bn/sc"
-  top: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b"
-  name: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/double3x3b/bn/sc"
-  top: "inception_4c/double3x3b/bn/sc"
-  name: "inception_4c/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4b/output"
-  top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4c/pool"
-  top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/pool_proj/bn/sc"
-  name: "inception_4c/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/1x1/bn/sc"
-  bottom: "inception_4c/3x3/bn/sc"
-  bottom: "inception_4c/double3x3b/bn/sc"
-  bottom: "inception_4c/pool_proj/bn/sc"
-  top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/1x1"
-  name: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  top: "inception_4d/1x1/bn/sc"
-  name: "inception_4d/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3_reduce/bn/sc"
-  name: "inception_4d/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/3x3_reduce/bn/sc"
-  top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/3x3"
-  name: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/3x3/bn/sc"
-  top: "inception_4d/3x3/bn/sc"
-  name: "inception_4d/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4c/output"
-  top: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce"
-  name: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3_reduce/bn/sc"
-  name: "inception_4d/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3_reduce/bn/sc"
-  top: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a"
-  name: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3a/bn/sc"
-  name: "inception_4d/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/double3x3a/bn/sc"
-  top: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b"
-  name: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/double3x3b/bn/sc"
-  top: "inception_4d/double3x3b/bn/sc"
-  name: "inception_4d/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4c/output"
-  top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_4d/pool"
-  top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 96
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/pool_proj/bn/sc"
-  name: "inception_4d/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/1x1/bn/sc"
-  bottom: "inception_4d/3x3/bn/sc"
-  bottom: "inception_4d/double3x3b/bn/sc"
-  bottom: "inception_4d/pool_proj/bn/sc"
-  top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3_reduce/bn/sc"
-  name: "inception_4e/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/3x3_reduce/bn/sc"
-  top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/3x3"
-  name: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  top: "inception_4e/3x3/bn/sc"
-  name: "inception_4e/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4d/output"
-  top: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce"
-  name: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3_reduce/bn/sc"
-  name: "inception_4e/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3_reduce/bn/sc"
-  top: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a"
-  name: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3a/bn/sc"
-  name: "inception_4e/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/double3x3a/bn/sc"
-  top: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b"
-  name: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_4e/double3x3b/bn/sc"
-  top: "inception_4e/double3x3b/bn/sc"
-  name: "inception_4e/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4d/output"
-  top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  bottom: "inception_4e/3x3/bn/sc"
-  bottom: "inception_4e/double3x3b/bn/sc"
-  bottom: "inception_4e/pool"
-  top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "pool4/5x5_s3"
-  name: "pool4/5x5_s3"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 5
-    stride: 3
-  }
-}
-layer {
- bottom: "pool4/5x5_s3"
-  top: "loss2/conv"
-  name: "loss2/conv"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/conv"
-  name: "loss2/conv/bn"
-  top: "loss2/conv/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/conv/bn/sc"
-  name: "loss2/conv/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/conv/bn/sc"
-  top: "loss2/fc"
-  name: "loss2/fc"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  inner_product_param {
-    num_output: 1024
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "loss2/fc"
-  name: "loss2/fc/bn"
-  top: "loss2/fc/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/fc/bn/sc"
-  name: "loss2/fc/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "loss2/fc/bn/sc"
-  top: "loss2/classifier"
-  name: "loss2/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss2/classifier"
-  bottom: "label"
-  top: "loss2/loss"
-  name: "loss2/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 0.3
-}
-layer {
-  bottom: "loss2/classifier"
-  top: "loss2/prob"
-  name: "loss2/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-1"
-  name: "loss2/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss2/prob"
-  bottom: "label"
-  top: "loss2/top-5"
-  name: "loss2/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/1x1"
-  name: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  top: "inception_5a/1x1/bn/sc"
-  name: "inception_5a/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3_reduce/bn/sc"
-  name: "inception_5a/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/3x3_reduce/bn/sc"
-  top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/3x3"
-  name: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/3x3/bn/sc"
-  top: "inception_5a/3x3/bn/sc"
-  name: "inception_5a/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_4e/output"
-  top: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 160
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce"
-  name: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3_reduce/bn/sc"
-  name: "inception_5a/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3_reduce/bn/sc"
-  top: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a"
-  name: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3a/bn/sc"
-  name: "inception_5a/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/double3x3a/bn/sc"
-  top: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b"
-  name: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/double3x3b/bn/sc"
-  top: "inception_5a/double3x3b/bn/sc"
-  name: "inception_5a/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_4e/output"
-  top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5a/pool"
-  top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/pool_proj/bn/sc"
-  name: "inception_5a/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/1x1/bn/sc"
-  bottom: "inception_5a/3x3/bn/sc"
-  bottom: "inception_5a/double3x3b/bn/sc"
-  bottom: "inception_5a/pool_proj/bn/sc"
-  top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 352
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/1x1"
-  name: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  top: "inception_5b/1x1/bn/sc"
-  name: "inception_5b/1x1/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3_reduce/bn/sc"
-  name: "inception_5b/3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/3x3_reduce/bn/sc"
-  top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 320
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/3x3"
-  name: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/3x3/bn/sc"
-  top: "inception_5b/3x3/bn/sc"
-  name: "inception_5b/3x3/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5a/output"
-  top: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 192
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce"
-  name: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3_reduce/bn/sc"
-  name: "inception_5b/double3x3_reduce/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3_reduce/bn/sc"
-  top: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a"
-  name: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3a/bn/sc"
-  name: "inception_5b/double3x3a/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
- bottom: "inception_5b/double3x3a/bn/sc"
-  top: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 224
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b"
-  name: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/double3x3b/bn/sc"
-  top: "inception_5b/double3x3b/bn/sc"
-  name: "inception_5b/double3x3b/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5a/output"
-  top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: MAX
-    kernel_size: 3
-    stride: 1
-    pad: 1
-  }
-}
-layer {
- bottom: "inception_5b/pool"
-  top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: "Convolution"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  convolution_param {
-    
-    num_output: 128
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_term: false
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn"
-  type: "BatchNorm"
-  batch_norm_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc"
-  type: "Scale"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/pool_proj/bn/sc"
-  name: "inception_5b/pool_proj/bn/sc/relu"
-  type: "ReLU"
-  relu_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/1x1/bn/sc"
-  bottom: "inception_5b/3x3/bn/sc"
-  bottom: "inception_5b/double3x3b/bn/sc"
-  bottom: "inception_5b/pool_proj/bn/sc"
-  top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: "Concat"
-  concat_param {
-    
-  }
-}
-layer {
-  bottom: "inception_5b/output"
-  top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: "Pooling"
-  pooling_param {
-    
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  bottom: "pool5/7x7_s1"
-  top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: "InnerProduct"
-  param {
-    lr_mult: 1
-    decay_mult: 1
-  }
-  param {
-    lr_mult: 2
-    decay_mult: 0
-  }
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layer {
-  bottom: "loss3/classifier"
-  bottom: "label"
-  top: "loss3/loss"
-  name: "loss3/loss"
-  type: "SoftmaxWithLoss"
-  loss_weight: 1
-}
-layer {
-  bottom: "loss3/classifier"
-  top: "loss3/prob"
-  name: "loss3/prob"
-  type: "Softmax"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-1"
-  name: "loss3/top-1"
-  type: "Accuracy"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  bottom: "loss3/prob"
-  bottom: "label"
-  top: "loss3/top-5"
-  name: "loss3/top-5"
-  type: "Accuracy"
-  accuracy_param {
-    top_k: 5
-  }
-  include {
-    phase: TEST
-  }
-}
diff --git a/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt
new file mode 100644
index 000000000..f6366fd27
--- /dev/null
+++ b/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt
@@ -0,0 +1,21661 @@
+# For INT8 reference
+name: "InceptionV3"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.0099999997764825821
+    }
+    shape {
+      dim: 64
+      dim: 3
+      dim: 299
+      dim: 299
+    }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "label"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+    }
+    shape {
+      dim: 64
+    }
+  }
+}
+layer {
+  name: "conv_conv2d"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.00999999977648
+    }
+  }
+}
+layer {
+  name: "conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_conv2d"
+  top: "conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_relu"
+  type: "ReLU"
+  bottom: "conv_conv2d_bn"
+  top: "conv_conv2d_relu"
+}
+layer {
+  name: "conv_1_1_conv2d"
+  type: "Convolution"
+  bottom: "conv_conv2d_relu"
+  top: "conv_1_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.00999999977648
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 75.3399963379
+    scale_out: 48.9399986267
+    scale_params: 1197.69995117
+    scale_params: 851.570007324
+    scale_params: 139.970001221
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+    scale_params: 132.100006104
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+    scale_params: 132.100006104
+    scale_params: 125.419998169
+    scale_params: 125.419998169
+    scale_params: 125.419998169
+    scale_params: 125.419998169
+    scale_params: 125.419998169
+  }
+}
+layer {
+  name: "conv_1_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_1_1_conv2d"
+  top: "conv_1_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_1_1_relu"
+  type: "ReLU"
+  bottom: "conv_1_1_conv2d_bn"
+  top: "conv_1_1_conv2d_relu"
+}
+layer {
+  name: "conv_2_2_conv2d"
+  type: "Convolution"
+  bottom: "conv_1_1_conv2d_relu"
+  top: "conv_2_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.00999999977648
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 48.9399986267
+    scale_out: 68.1200027466
+    scale_params: 815.640014648
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+    scale_params: 238.300003052
+    scale_params: 238.300003052
+    scale_params: 238.300003052
+  }
+}
+layer {
+  name: "conv_2_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_2_2_conv2d"
+  top: "conv_2_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_2_2_relu"
+  type: "ReLU"
+  bottom: "conv_2_2_conv2d_bn"
+  top: "conv_2_2_conv2d_relu"
+}
+layer {
+  name: "pool"
+  type: "Pooling"
+  bottom: "conv_2_2_conv2d_relu"
+  top: "pool"
+  pooling_param {
+    pool: MAX
+    pad: 0
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv_3_3_conv2d"
+  type: "Convolution"
+  bottom: "pool"
+  top: "conv_3_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 80
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.00999999977648
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 68.1200027466
+    scale_out: 74.3199996948
+    scale_params: 335.029998779
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+}
+layer {
+  name: "conv_3_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_3_3_conv2d"
+  top: "conv_3_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_3_3_relu"
+  type: "ReLU"
+  bottom: "conv_3_3_conv2d_bn"
+  top: "conv_3_3_conv2d_relu"
+}
+layer {
+  name: "conv_4_4_conv2d"
+  type: "Convolution"
+  bottom: "conv_3_3_conv2d_relu"
+  top: "conv_4_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.00999999977648
+    }
+  }
+  quantization_param {
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+    bw_layer_in: 8
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+  top: "mixed_conv_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_conv_conv2d_bn"
+  top: "mixed_conv_conv2d_relu"
+}
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+}
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+  top: "mixed_tower_conv_1_conv2d_relu"
+}
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+  top: "mixed_tower_1_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  bottom: "mixed_tower_1_conv_conv2d_bn"
+  top: "mixed_tower_1_conv_conv2d_relu"
+}
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+  top: "mixed_tower_1_conv_1_conv2d"
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+}
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+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_1_conv2d_bn"
+  top: "mixed_tower_1_conv_1_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_tower_1_conv_1_conv2d_relu"
+  top: "mixed_tower_1_conv_2_conv2d"
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+  bottom: "mixed_tower_1_conv_2_conv2d"
+  top: "mixed_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_2_conv2d_bn"
+  top: "mixed_tower_1_conv_2_conv2d_relu"
+}
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+  type: "Pooling"
+  bottom: "pool1"
+  top: "AVE_pool_mixed_pool"
+  pooling_param {
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+    kernel_size: 3
+    stride: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_pool"
+  top: "mixed_tower_2_conv_conv2d"
+  param {
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+  convolution_param {
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+  type: "BatchNorm"
+  bottom: "mixed_tower_2_conv_conv2d"
+  top: "mixed_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_tower_2_conv_conv2d_bn"
+  top: "mixed_tower_2_conv_conv2d_relu"
+}
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+  type: "Concat"
+  bottom: "mixed_conv_conv2d_relu"
+  bottom: "mixed_tower_conv_1_conv2d_relu"
+  bottom: "mixed_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_chconcat"
+  concat_param {
+    axis: 1
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+}
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+  name: "mixed_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_conv_conv2d"
+  param {
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+}
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+  type: "BatchNorm"
+  bottom: "mixed_1_conv_conv2d"
+  top: "mixed_1_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_conv_conv2d_bn"
+  top: "mixed_1_conv_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_tower_conv_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_1_tower_conv_conv2d"
+  top: "mixed_1_tower_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_tower_conv_conv2d_bn"
+  top: "mixed_1_tower_conv_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_1_tower_conv_conv2d_relu"
+  top: "mixed_1_tower_conv_1_conv2d"
+  param {
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+}
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+  type: "BatchNorm"
+  bottom: "mixed_1_tower_conv_1_conv2d"
+  top: "mixed_1_tower_conv_1_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_tower_conv_1_conv2d_bn"
+  top: "mixed_1_tower_conv_1_conv2d_relu"
+}
+layer {
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_tower_1_conv_conv2d"
+  param {
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+}
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+  name: "mixed_1_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_1_conv_conv2d"
+  top: "mixed_1_tower_1_conv_conv2d_bn"
+  batch_norm_param {
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+}
+layer {
+  name: "mixed_1_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_conv2d_bn"
+  top: "mixed_1_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_1_tower_1_conv_conv2d_relu"
+  top: "mixed_1_tower_1_conv_1_conv2d"
+  param {
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+    decay_mult: 1.0
+  }
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+    pad: 1
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+    stride: 1
+    weight_filler {
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+}
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+  bottom: "mixed_1_tower_1_conv_1_conv2d_bn"
+  top: "mixed_1_tower_1_conv_1_conv2d_relu"
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+  bottom: "mixed_1_tower_1_conv_2_conv2d"
+  top: "mixed_1_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_bn"
+  top: "mixed_1_tower_1_conv_2_conv2d_relu"
+}
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+  type: "Pooling"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "AVE_pool_mixed_1_pool"
+  pooling_param {
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+    kernel_size: 3
+    stride: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_1_pool"
+  top: "mixed_1_tower_2_conv_conv2d"
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+  type: "BatchNorm"
+  bottom: "mixed_1_tower_2_conv_conv2d"
+  top: "mixed_1_tower_2_conv_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_tower_2_conv_conv2d_bn"
+  top: "mixed_1_tower_2_conv_conv2d_relu"
+}
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+  type: "Concat"
+  bottom: "mixed_1_conv_conv2d_relu"
+  bottom: "mixed_1_tower_conv_1_conv2d_relu"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_1_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_1_chconcat"
+  concat_param {
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+}
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+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_conv_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_2_conv_conv2d"
+  top: "mixed_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_conv_conv2d_bn"
+  top: "mixed_2_conv_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_conv_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_2_tower_conv_conv2d"
+  top: "mixed_2_tower_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_conv2d_bn"
+  top: "mixed_2_tower_conv_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_2_tower_conv_conv2d_relu"
+  top: "mixed_2_tower_conv_1_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_2_tower_conv_1_conv2d"
+  top: "mixed_2_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
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+  name: "mixed_2_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_1_conv2d_bn"
+  top: "mixed_2_tower_conv_1_conv2d_relu"
+}
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+  name: "mixed_2_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_1_conv_conv2d"
+  param {
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+    decay_mult: 1.0
+  }
+  convolution_param {
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+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
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+  }
+  quantization_param {
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+}
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+  name: "mixed_2_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_conv2d"
+  top: "mixed_2_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_conv2d_bn"
+  top: "mixed_2_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_2_tower_1_conv_conv2d_relu"
+  top: "mixed_2_tower_1_conv_1_conv2d"
+  param {
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+    decay_mult: 1.0
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+  convolution_param {
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+    pad: 1
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+}
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+  name: "mixed_2_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_1_conv2d"
+  top: "mixed_2_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_2_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_1_conv2d_bn"
+  top: "mixed_2_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_2_tower_1_conv_1_conv2d_relu"
+  top: "mixed_2_tower_1_conv_2_conv2d"
+  param {
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+  convolution_param {
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+}
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+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_2_conv2d"
+  top: "mixed_2_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_2_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_2_conv2d_bn"
+  top: "mixed_2_tower_1_conv_2_conv2d_relu"
+}
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+  name: "AVE_pool_mixed_2_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "AVE_pool_mixed_2_pool"
+  pooling_param {
+    pool: AVE
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "mixed_2_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_2_pool"
+  top: "mixed_2_tower_2_conv_conv2d"
+  param {
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+    decay_mult: 1.0
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+  convolution_param {
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+    pad: 0
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+    weight_filler {
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+}
+layer {
+  name: "mixed_2_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_2_conv_conv2d"
+  top: "mixed_2_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
+layer {
+  name: "mixed_2_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_2_conv_conv2d_bn"
+  top: "mixed_2_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_2_chconcat"
+  type: "Concat"
+  bottom: "mixed_2_conv_conv2d_relu"
+  bottom: "mixed_2_tower_conv_1_conv2d_relu"
+  bottom: "mixed_2_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_2_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_2_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_3_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "mixed_3_conv_conv2d"
+  param {
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+    decay_mult: 1.0
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+  convolution_param {
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+    pad: 0
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+    stride: 2
+    weight_filler {
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+  bottom: "mixed_3_tower_conv_conv2d_bn"
+  top: "mixed_3_tower_conv_conv2d_relu"
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+  type: "BatchNorm"
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+  top: "mixed_3_tower_conv_1_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_3_tower_conv_1_conv2d_bn"
+  top: "mixed_3_tower_conv_1_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_3_tower_conv_1_conv2d_relu"
+  top: "mixed_3_tower_conv_2_conv2d"
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+}
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+  bottom: "mixed_3_tower_conv_2_conv2d_bn"
+  top: "mixed_3_tower_conv_2_conv2d_relu"
+}
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+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "max_pool_mixed_3_pool"
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+}
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+  bottom: "max_pool_mixed_3_pool"
+  bottom: "mixed_3_conv_conv2d_relu"
+  bottom: "mixed_3_tower_conv_2_conv2d_relu"
+  top: "ch_concat_mixed_3_chconcat"
+  concat_param {
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+  top: "mixed_4_conv_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_4_conv_conv2d_bn"
+  top: "mixed_4_conv_conv2d_relu"
+}
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+  top: "mixed_4_tower_conv_conv2d"
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+  top: "mixed_4_tower_1_conv_4_conv2d_bn"
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+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "AVE_pool_mixed_4_pool"
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+  bottom: "AVE_pool_mixed_4_pool"
+  top: "mixed_4_tower_2_conv_conv2d"
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+  top: "mixed_4_tower_2_conv_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_4_tower_2_conv_conv2d_bn"
+  top: "mixed_4_tower_2_conv_conv2d_relu"
+}
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+  bottom: "mixed_4_conv_conv2d_relu"
+  bottom: "mixed_4_tower_conv_2_conv2d_relu"
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+  bottom: "mixed_4_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_4_chconcat"
+  concat_param {
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+  type: "ReLU"
+  bottom: "mixed_5_conv_conv2d_bn"
+  top: "mixed_5_conv_conv2d_relu"
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+  top: "mixed_5_tower_conv_conv2d"
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+  bottom: "mixed_5_tower_conv_conv2d_bn"
+  top: "mixed_5_tower_conv_conv2d_relu"
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+  top: "mixed_5_tower_conv_1_conv2d_relu"
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+  bottom: "mixed_5_tower_1_conv_conv2d_bn"
+  top: "mixed_5_tower_1_conv_conv2d_relu"
+}
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+  bottom: "mixed_5_tower_1_conv_conv2d_relu"
+  top: "mixed_5_tower_1_conv_1_conv2d"
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+  bottom: "mixed_5_tower_1_conv_1_conv2d_bn"
+  top: "mixed_5_tower_1_conv_1_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_1_conv2d_relu"
+  top: "mixed_5_tower_1_conv_2_conv2d"
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+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_3_conv2d"
+  top: "mixed_5_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_3_conv2d_bn"
+  top: "mixed_5_tower_1_conv_3_conv2d_relu"
+}
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+  name: "mixed_5_tower_1_conv_4_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_3_conv2d_relu"
+  top: "mixed_5_tower_1_conv_4_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_4_conv2d"
+  top: "mixed_5_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_5_tower_1_conv_4_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_4_conv2d_bn"
+  top: "mixed_5_tower_1_conv_4_conv2d_relu"
+}
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+  name: "AVE_pool_mixed_5_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_4_chconcat"
+  top: "AVE_pool_mixed_5_pool"
+  pooling_param {
+    pool: AVE
+    pad: 1
+    kernel_size: 3
+    stride: 1
+  }
+}
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+  name: "mixed_5_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_5_pool"
+  top: "mixed_5_tower_2_conv_conv2d"
+  param {
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+    decay_mult: 1.0
+  }
+  convolution_param {
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+    pad: 0
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+    stride: 1
+    weight_filler {
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+  }
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+  name: "mixed_5_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_2_conv_conv2d"
+  top: "mixed_5_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_5_tower_2_conv_conv2d_bn"
+  top: "mixed_5_tower_2_conv_conv2d_relu"
+}
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+  name: "ch_concat_mixed_5_chconcat"
+  type: "Concat"
+  bottom: "mixed_5_conv_conv2d_relu"
+  bottom: "mixed_5_tower_conv_2_conv2d_relu"
+  bottom: "mixed_5_tower_1_conv_4_conv2d_relu"
+  bottom: "mixed_5_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_5_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_6_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "mixed_6_conv_conv2d"
+  param {
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+    decay_mult: 1.0
+  }
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+    pad: 0
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+    weight_filler {
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+  top: "mixed_6_tower_1_conv_1_conv2d_relu"
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+  bottom: "mixed_6_tower_1_conv_1_conv2d_relu"
+  top: "mixed_6_tower_1_conv_2_conv2d"
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+  bottom: "mixed_6_tower_conv_2_conv2d_relu"
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+  top: "ch_concat_mixed_6_chconcat"
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+  bottom: "mixed_7_tower_conv_conv2d_bn"
+  top: "mixed_7_tower_conv_conv2d_relu"
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+  type: "BatchNorm"
+  bottom: "mixed_7_tower_conv_1_conv2d"
+  top: "mixed_7_tower_conv_1_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_7_tower_conv_1_conv2d_bn"
+  top: "mixed_7_tower_conv_1_conv2d_relu"
+}
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+  name: "mixed_7_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_conv_1_conv2d_relu"
+  top: "mixed_7_tower_conv_2_conv2d"
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+  name: "mixed_7_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_conv_2_conv2d"
+  top: "mixed_7_tower_conv_2_conv2d_bn"
+  batch_norm_param {
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+  name: "mixed_7_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_conv_2_conv2d_bn"
+  top: "mixed_7_tower_conv_2_conv2d_relu"
+}
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+  name: "mixed_7_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "mixed_7_tower_1_conv_conv2d"
+  param {
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+  bottom: "mixed_7_tower_1_conv_2_conv2d_bn"
+  top: "mixed_7_tower_1_conv_2_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_2_conv2d_relu"
+  top: "mixed_7_tower_1_conv_3_conv2d"
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+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_3_conv2d"
+  top: "mixed_7_tower_1_conv_3_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_3_conv2d_bn"
+  top: "mixed_7_tower_1_conv_3_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_3_conv2d_relu"
+  top: "mixed_7_tower_1_conv_4_conv2d"
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+  name: "mixed_7_tower_1_conv_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_4_conv2d"
+  top: "mixed_7_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_4_conv2d_bn"
+  top: "mixed_7_tower_1_conv_4_conv2d_relu"
+}
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+  type: "Pooling"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "AVE_pool_mixed_7_pool"
+  pooling_param {
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+    kernel_size: 3
+    stride: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_7_pool"
+  top: "mixed_7_tower_2_conv_conv2d"
+  param {
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+  name: "mixed_9_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_conv_conv2d"
+  top: "mixed_9_tower_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_9_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_conv_conv2d_bn"
+  top: "mixed_9_tower_conv_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_9_tower_conv_conv2d_relu"
+  top: "mixed_9_tower_mixed_conv_conv2d"
+  param {
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+  convolution_param {
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+  top: "mixed_9_tower_mixed_conv_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_9_tower_mixed_conv_conv2d_bn"
+  top: "mixed_9_tower_mixed_conv_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_9_tower_conv_conv2d_relu"
+  top: "mixed_9_tower_mixed_conv_1_conv2d"
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+  top: "mixed_9_tower_1_conv_conv2d_relu"
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+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu"
+}
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+  type: "Pooling"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "AVE_pool_mixed_9_pool"
+  pooling_param {
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+    pad: 1
+    kernel_size: 3
+    stride: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_9_pool"
+  top: "mixed_9_tower_2_conv_conv2d"
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+  name: "mixed_9_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_2_conv_conv2d"
+  top: "mixed_9_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
+layer {
+  name: "mixed_9_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_2_conv_conv2d_bn"
+  top: "mixed_9_tower_2_conv_conv2d_relu"
+}
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+  name: "ch_concat_mixed_9_chconcat"
+  type: "Concat"
+  bottom: "mixed_9_conv_conv2d_relu"
+  bottom: "mixed_9_tower_mixed_conv_conv2d_relu"
+  bottom: "mixed_9_tower_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_9_tower_1_mixed_conv_conv2d_relu"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_9_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_9_chconcat"
+  concat_param {
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+}
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+  name: "mixed_10_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "mixed_10_conv_conv2d"
+  param {
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+  type: "ReLU"
+  bottom: "mixed_10_tower_mixed_conv_conv2d_bn"
+  top: "mixed_10_tower_mixed_conv_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_10_tower_conv_conv2d_relu"
+  top: "mixed_10_tower_mixed_conv_1_conv2d"
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+  top: "mixed_10_tower_1_mixed_conv_conv2d_bn"
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+  type: "ReLU"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn"
+  top: "mixed_10_tower_1_mixed_conv_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_10_tower_1_conv_1_conv2d_relu"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d"
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+    scale_params: 82.7200012207
+    scale_params: 82.7200012207
+    scale_params: 82.7200012207
+    scale_params: 81.5699996948
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+    scale_params: 81.5699996948
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+    scale_params: 81.5699996948
+    scale_params: 81.5699996948
+    scale_params: 81.5699996948
+    scale_params: 81.5699996948
+    scale_params: 81.5699996948
+    scale_params: 81.5699996948
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_2_conv_conv2d"
+  top: "mixed_10_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_2_conv_conv2d_bn"
+  top: "mixed_10_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_10_chconcat"
+  type: "Concat"
+  bottom: "mixed_10_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_10_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "global_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_10_chconcat"
+  top: "global_pool"
+  pooling_param {
+    pool: AVE
+    pad: 0
+    kernel_size: 8
+    stride: 1
+  }
+}
+layer {
+  name: "drop"
+  type: "Dropout"
+  bottom: "global_pool"
+  top: "global_pool"
+  dropout_param {
+    dropout_ratio: 0.800000011921
+  }
+}
+layer {
+  name: "flatten"
+  type: "Flatten"
+  bottom: "global_pool"
+  top: "flatten"
+}
+layer {
+  name: "fc1"
+  type: "InnerProduct"
+  bottom: "flatten"
+  top: "fc1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
diff --git a/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt b/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt
new file mode 100644
index 000000000..3aab1c6b9
--- /dev/null
+++ b/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt
@@ -0,0 +1,29478 @@
+# For INT8 reference
+name: "ResNet-50"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape {
+      dim: 64
+      dim: 3
+      dim: 224
+      dim: 224
+    }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "label"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+    }
+    shape {
+      dim: 64
+    }
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.20000000298
+    }
+  }
+}
+layer {
+  name: "bn_conv1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "conv1"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "scale_conv1"
+  type: "Scale"
+  bottom: "conv1"
+  top: "conv1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_relu"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+  relu_param {
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "res2a_branch1"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "res2a_branch1"
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.20000000298
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 8.38000011444
+    scale_out: 6.34000015259
+    scale_params: 3828.66992188
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+    scale_params: 75.9199981689
+  }
+}
+layer {
+  name: "bn2a_branch1"
+  type: "BatchNorm"
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "scale2a_branch1"
+  type: "Scale"
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "res2a_branch2a"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "res2a_branch2a"
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.20000000298
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
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+}
+layer {
+  name: "bn2a_branch2a"
+  type: "BatchNorm"
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "scale2a_branch2a"
+  type: "Scale"
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "res2a_branch2a_relu"
+  type: "ReLU"
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  relu_param {
+  }
+}
+layer {
+  name: "res2a_branch2b"
+  type: "Convolution"
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2b"
+  convolution_param {
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+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.20000000298
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+  }
+  quantization_param {
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+  top: "res2a_branch2c"
+  scale_param {
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+  bottom: "res2a_branch2c"
+  top: "res2a"
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+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  scale_param {
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+  top: "res2b_branch2a"
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+  top: "res3b_branch2a"
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+  top: "res3b_branch2a"
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+  type: "BatchNorm"
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  batch_norm_param {
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+  type: "Scale"
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  scale_param {
+    bias_term: true
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+}
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+  bottom: "res3d"
+  top: "res4a_branch2a"
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+  top: "res4d_branch2c"
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+  bottom: "res4d_branch2c"
+  top: "res4d_branch2c"
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+  bottom: "res4c"
+  bottom: "res4d_branch2c"
+  top: "res4d"
+  eltwise_param {
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+  type: "ReLU"
+  bottom: "res4d"
+  top: "res4d"
+  relu_param {
+  }
+}
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+  type: "Convolution"
+  bottom: "res4d"
+  top: "res4e_branch2a"
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+  type: "BatchNorm"
+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
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+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
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+  bottom: "res5a_branch1"
+  bottom: "res5a_branch2c"
+  top: "res5a"
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+  bottom: "res5a"
+  top: "res5a"
+  relu_param {
+  }
+}
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+  top: "res5b_branch2a"
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+    scale_params: 23.2399997711
+  }
+}
+layer {
+  name: "bn5c_branch2c"
+  type: "BatchNorm"
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "scale5c_branch2c"
+  type: "Scale"
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "res5c"
+  type: "Eltwise"
+  bottom: "res5b"
+  bottom: "res5c_branch2c"
+  top: "res5c"
+  eltwise_param {
+  }
+}
+layer {
+  name: "res5c_relu"
+  type: "ReLU"
+  bottom: "res5c"
+  top: "res5c"
+  relu_param {
+  }
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "res5c"
+  top: "pool5"
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layer {
+  name: "fc1000"
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc1000"
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
diff --git a/models/intel_optimized_models/int8/ssd_int8_mc.prototxt b/models/intel_optimized_models/int8/ssd_int8_mc.prototxt
new file mode 100644
index 000000000..67788a664
--- /dev/null
+++ b/models/intel_optimized_models/int8/ssd_int8_mc.prototxt
@@ -0,0 +1,10798 @@
+# For INT8 reference
+name: "VGG_VOC0712_SSD_300x300_test"
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 0.01
+    }
+    shape {
+      dim: 32
+      dim: 3
+      dim: 300
+      dim: 300
+    }
+  }
+}
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "label"
+  dummy_data_param {
+    data_filler {
+      type: "constant"
+      value: 1
+    }
+    shape {
+      dim: 1
+      dim: 1
+      dim: 1
+      dim: 8
+    }
+  }
+}
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 0.319999992847
+    scale_out: 0.0799999982119
+    scale_params: 619.210021973
+    scale_params: 619.210021973
+    scale_params: 619.210021973
+    scale_params: 619.210021973
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+}
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+  type: "ReLU"
+  bottom: "conv9_2"
+  top: "conv9_2"
+}
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+  name: "conv4_3_norm"
+  type: "Normalize"
+  bottom: "conv4_3"
+  top: "conv4_3_norm"
+  norm_param {
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+    scale_filler {
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+      value: 20.0
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+    channel_shared: false
+  }
+}
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+  name: "conv4_3_norm_mbox_loc"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_loc"
+  param {
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+  param {
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+  convolution_param {
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+    stride: 1
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+    bias_filler {
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+  }
+  quantization_param {
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+}
+layer {
+  name: "conv4_3_norm_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_loc"
+  top: "conv4_3_norm_mbox_loc_perm"
+  permute_param {
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+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_loc_perm"
+  top: "conv4_3_norm_mbox_loc_flat"
+  flatten_param {
+    axis: 1
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+}
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+  name: "conv4_3_norm_mbox_conf"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_conf"
+  param {
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+    bias_filler {
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+  quantization_param {
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+}
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+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_conf"
+  top: "conv4_3_norm_mbox_conf_perm"
+  permute_param {
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+    order: 3
+    order: 1
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+}
+layer {
+  name: "conv4_3_norm_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_conf_perm"
+  top: "conv4_3_norm_mbox_conf_flat"
+  flatten_param {
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+}
+layer {
+  name: "conv4_3_norm_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv4_3_norm"
+  bottom: "data"
+  top: "conv4_3_norm_mbox_priorbox"
+  prior_box_param {
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+    max_size: 60.0
+    aspect_ratio: 2.0
+    flip: true
+    clip: false
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+    step: 8.0
+    offset: 0.5
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+}
+layer {
+  name: "fc7_mbox_loc"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_loc"
+  param {
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+    decay_mult: 1.0
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+  param {
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+  convolution_param {
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+    stride: 1
+    weight_filler {
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+    }
+    bias_filler {
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+  }
+  quantization_param {
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+}
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+  type: "Permute"
+  bottom: "fc7_mbox_loc"
+  top: "fc7_mbox_loc_perm"
+  permute_param {
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+    order: 2
+    order: 3
+    order: 1
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+}
+layer {
+  name: "fc7_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_loc_perm"
+  top: "fc7_mbox_loc_flat"
+  flatten_param {
+    axis: 1
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+}
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+  type: "Convolution"
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+  top: "fc7_mbox_conf"
+  param {
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+    bias_filler {
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+}
+layer {
+  name: "fc7_mbox_conf_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_conf"
+  top: "fc7_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_conf_perm"
+  top: "fc7_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "fc7"
+  bottom: "data"
+  top: "fc7_mbox_priorbox"
+  prior_box_param {
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+    max_size: 111.0
+    aspect_ratio: 2.0
+    aspect_ratio: 3.0
+    flip: true
+    clip: false
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+    step: 16.0
+    offset: 0.5
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+}
+layer {
+  name: "conv6_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv6_2"
+  top: "conv6_2_mbox_loc"
+  param {
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+  param {
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+    bias_filler {
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+  }
+  quantization_param {
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+}
+layer {
+  name: "conv6_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_loc"
+  top: "conv6_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_loc_perm"
+  top: "conv6_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv6_2"
+  top: "conv6_2_mbox_conf"
+  param {
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+    weight_filler {
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+    }
+    bias_filler {
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+  }
+  quantization_param {
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+  top: "conv8_2_mbox_loc_flat"
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+  type: "Permute"
+  bottom: "conv8_2_mbox_conf"
+  top: "conv8_2_mbox_conf_perm"
+  permute_param {
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+}
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+  type: "Flatten"
+  bottom: "conv8_2_mbox_conf_perm"
+  top: "conv8_2_mbox_conf_flat"
+  flatten_param {
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+}
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+  type: "PriorBox"
+  bottom: "conv8_2"
+  bottom: "data"
+  top: "conv8_2_mbox_priorbox"
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+}
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+  type: "Convolution"
+  bottom: "conv9_2"
+  top: "conv9_2_mbox_loc"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 10.6199998856
+    scale_out: 35.0900001526
+    scale_params: 1395.75
+    scale_params: 1395.75
+    scale_params: 1395.75
+    scale_params: 1182.09997559
+    scale_params: 1182.09997559
+    scale_params: 1182.09997559
+    scale_params: 1182.09997559
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+    scale_params: 1117.32995605
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_loc"
+  top: "conv9_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_loc_perm"
+  top: "conv9_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv9_2"
+  top: "conv9_2_mbox_conf"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  convolution_param {
+    num_output: 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+  quantization_param {
+    precision: DYNAMIC_FIXED_POINT
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 10.6199998856
+    scale_out: 5.92000007629
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 806.66998291
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 718.020019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+    scale_params: 539.520019531
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_conf"
+  top: "conv9_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_conf_perm"
+  top: "conv9_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv9_2"
+  bottom: "data"
+  top: "conv9_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 264.0
+    max_size: 315.0
+    aspect_ratio: 2.0
+    flip: true
+    clip: false
+    variance: 0.10000000149
+    variance: 0.10000000149
+    variance: 0.20000000298
+    variance: 0.20000000298
+    step: 300.0
+    offset: 0.5
+  }
+}
+layer {
+  name: "mbox_loc"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_loc_flat"
+  bottom: "fc7_mbox_loc_flat"
+  bottom: "conv6_2_mbox_loc_flat"
+  bottom: "conv7_2_mbox_loc_flat"
+  bottom: "conv8_2_mbox_loc_flat"
+  bottom: "conv9_2_mbox_loc_flat"
+  top: "mbox_loc"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_conf"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_conf_flat"
+  bottom: "fc7_mbox_conf_flat"
+  bottom: "conv6_2_mbox_conf_flat"
+  bottom: "conv7_2_mbox_conf_flat"
+  bottom: "conv8_2_mbox_conf_flat"
+  bottom: "conv9_2_mbox_conf_flat"
+  top: "mbox_conf"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_priorbox"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_priorbox"
+  bottom: "fc7_mbox_priorbox"
+  bottom: "conv6_2_mbox_priorbox"
+  bottom: "conv7_2_mbox_priorbox"
+  bottom: "conv8_2_mbox_priorbox"
+  bottom: "conv9_2_mbox_priorbox"
+  top: "mbox_priorbox"
+  concat_param {
+    axis: 2
+  }
+}
+layer {
+  name: "mbox_conf_reshape"
+  type: "Reshape"
+  bottom: "mbox_conf"
+  top: "mbox_conf_reshape"
+  reshape_param {
+    shape {
+      dim: 0
+      dim: -1
+      dim: 21
+    }
+  }
+}
+layer {
+  name: "mbox_conf_softmax"
+  type: "Softmax"
+  bottom: "mbox_conf_reshape"
+  top: "mbox_conf_softmax"
+  softmax_param {
+    axis: 2
+  }
+}
+layer {
+  name: "mbox_conf_flatten"
+  type: "Flatten"
+  bottom: "mbox_conf_softmax"
+  top: "mbox_conf_flatten"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "detection_out"
+  type: "DetectionOutput"
+  bottom: "mbox_loc"
+  bottom: "mbox_conf_flatten"
+  bottom: "mbox_priorbox"
+  top: "detection_out"
+  include {
+    phase: TEST
+  }
+  detection_output_param {
+    num_classes: 21
+    share_location: true
+    background_label_id: 0
+    nms_param {
+      nms_threshold: 0.449999988079
+      top_k: 400
+    }
+    code_type: CENTER_SIZE
+    keep_top_k: 200
+    confidence_threshold: 0.00999999977648
+  }
+}
diff --git a/models/intel_optimized_models/multinode/alexnet_bn_32nodes/train_val2.prototxt b/models/intel_optimized_models/multinode/alexnet_bn_32nodes/train_val2.prototxt
index 584f292b0..48046d17d 100644
--- a/models/intel_optimized_models/multinode/alexnet_bn_32nodes/train_val2.prototxt
+++ b/models/intel_optimized_models/multinode/alexnet_bn_32nodes/train_val2.prototxt
@@ -3,8 +3,6 @@
 #------------------------------------------
 name: "AlexNet_bn"
 
-engine:"MKL2017"
-
 #default_conv_algos_override: "1,1,1"
 #default_cudnn_math_override: 0
 
diff --git a/models/intel_optimized_models/multinode/alexnet_bn_64nodes/train_val2.prototxt b/models/intel_optimized_models/multinode/alexnet_bn_64nodes/train_val2.prototxt
index a7b789dbf..f02068fcc 100644
--- a/models/intel_optimized_models/multinode/alexnet_bn_64nodes/train_val2.prototxt
+++ b/models/intel_optimized_models/multinode/alexnet_bn_64nodes/train_val2.prototxt
@@ -3,8 +3,6 @@
 #------------------------------------------
 name: "AlexNet_bn"
 
-engine:"MKL2017"
-
 #default_conv_algos_override: "1,1,1"
 #default_cudnn_math_override: 0
 
diff --git a/models/intel_optimized_models/multinode/default_resnet50_16nodes/solver.prototxt b/models/intel_optimized_models/multinode/default_resnet50_16nodes/solver.prototxt
index 27054d7d2..b5f73dee9 100644
--- a/models/intel_optimized_models/multinode/default_resnet50_16nodes/solver.prototxt
+++ b/models/intel_optimized_models/multinode/default_resnet50_16nodes/solver.prototxt
@@ -1,6 +1,6 @@
 net: "models/intel_optimized_models/multinode/default_resnet50_16nodes/train_val.prototxt"
 test_iter: 1000
-test_interval: 625
+test_interval: 563
 test_initialization: false
 display: 40
 base_lr: 0.8
@@ -9,11 +9,11 @@ stepvalue:18750
 stepvalue:37500
 stepvalue:50000
 gamma: 0.1
-max_iter: 62556   # 56300
+max_iter: 56300
 warmup_iter: 3125 # 1281167 / 2048 * 5 epochs
 warmup_start_lr: 0.1
 momentum: 0.9
 weight_decay: 0.0001
-snapshot: 6250
+snapshot: 5630
 snapshot_prefix: "default_resnet_50_16_nodes"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/solver.prototxt b/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt
similarity index 54%
rename from models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/solver.prototxt
rename to models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt
index 4f4f21a93..1729d9320 100644
--- a/models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/solver.prototxt
+++ b/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt
@@ -1,6 +1,6 @@
-net: "models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/train_val.prototxt"
+net: "models/intel_optimized_models/multinode/default_resnet50_64nodes/train_val.prototxt"
 test_iter: 1000
-test_interval: 156
+test_interval: 140
 test_initialization: false
 display: 40
 base_lr: 3.2
@@ -14,6 +14,6 @@ warmup_iter: 780 # 1281167 / 8192 * 5 epochs
 warmup_start_lr: 0.1
 momentum: 0.9
 weight_decay: 0.0001
-snapshot: 156
-snapshot_prefix: "models/intel_optimized_models/multinode/resnet_50_64_nodes_8k_batch/resnet_50_64_nodes_8k"
+snapshot: 1408
+snapshot_prefix: "default_resnet_50_64_nodes"
 solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/train_val.prototxt b/models/intel_optimized_models/multinode/default_resnet50_64nodes/train_val.prototxt
similarity index 99%
rename from models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/train_val.prototxt
rename to models/intel_optimized_models/multinode/default_resnet50_64nodes/train_val.prototxt
index d98323ed6..de4efad2b 100644
--- a/models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/train_val.prototxt
+++ b/models/intel_optimized_models/multinode/default_resnet50_64nodes/train_val.prototxt
@@ -1,4 +1,5 @@
 name: "ResNet-50"
+bn_stats_batch_size: 32
 layer {
   name: "data"
   type: "Data"
@@ -25,7 +26,7 @@ layer {
   }
   data_param {
     source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 32
+    batch_size: 128
     backend: LMDB
     prefetch: 2
     shuffle: true
@@ -782,7 +783,7 @@ layer {
     num_output: 128
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -840,7 +841,7 @@ layer {
     num_output: 128
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -1568,7 +1569,7 @@ layer {
     num_output: 256
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -1626,7 +1627,7 @@ layer {
     num_output: 256
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -2726,7 +2727,7 @@ layer {
     num_output: 512
     kernel_size: 1
     pad: 0
-    stride: 1
+    stride: 2
     bias_term: false
     weight_filler {
       type: "msra"
@@ -2784,7 +2785,7 @@ layer {
     num_output: 512
     kernel_size: 3
     pad: 1
-    stride: 2
+    stride: 1
     bias_term: false
     weight_filler {
       type: "msra"
@@ -3301,6 +3302,9 @@ layer {
   top: "loss"
   name: "prob"
   type: "SoftmaxWithLoss"
+  include {
+      phase: TRAIN
+  }
 }
 layer {
   name: "loss3/top-1"
diff --git a/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt b/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt
new file mode 100644
index 000000000..6cab44e72
--- /dev/null
+++ b/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt
@@ -0,0 +1,17 @@
+net: "models/intel_optimized_models/multinode/default_resnet50_8nodes/train_val.prototxt"
+test_iter: 1000
+test_interval: 1126
+test_initialization: false
+display: 40
+base_lr: 0.4
+lr_policy: "multistep"
+stepvalue:37440
+stepvalue:74880
+stepvalue:99840
+gamma: 0.1
+max_iter: 112600
+momentum: 0.9
+weight_decay: 0.0001
+snapshot: 11260
+snapshot_prefix: "default_resnet_50_8_nodes"
+solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/train_val.prototxt b/models/intel_optimized_models/multinode/default_resnet50_8nodes/train_val.prototxt
similarity index 93%
rename from models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/train_val.prototxt
rename to models/intel_optimized_models/multinode/default_resnet50_8nodes/train_val.prototxt
index 75bff6269..de4efad2b 100644
--- a/models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/train_val.prototxt
+++ b/models/intel_optimized_models/multinode/default_resnet50_8nodes/train_val.prototxt
@@ -1,3322 +1,3325 @@
-name: "ResNet-50"
-bn_stats_batch_size: 32
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  transform_param {
-    mirror: true
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_aspect_ratio_param {
-      min_area_ratio: 0.08
-      max_area_ratio: 1
-      aspect_ratio_change: 0.75
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 128
-    backend: LMDB
-    prefetch: 2
-    shuffle: true
-  }
-}
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  transform_param {
-    mirror: false
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_resize_param {
-      min_size: 256
-      max_size: 256
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    batch_size: 50
-    backend: LMDB
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1"
-  name: "conv1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 7
-    pad: 3
-    stride: 2
-    weight_filler {
-      type: "msra"
-      variance_norm: FAN_OUT
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "bn_conv1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "scale_conv1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "conv1_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "pool1"
-  name: "pool1"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 3
-    stride: 2
-    pool: MAX
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch1"
-  name: "res2a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "bn2a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "scale2a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "bn2a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "scale2a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "bn2a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "scale2a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2c"
-  name: "res2a_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "bn2a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "scale2a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  bottom: "res2a_branch2c"
-  top: "res2a"
-  name: "res2a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2a"
-  name: "res2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "bn2b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "scale2b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "bn2b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "scale2b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2c"
-  name: "res2b_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "bn2b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "scale2b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a"
-  bottom: "res2b_branch2c"
-  top: "res2b"
-  name: "res2b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2b"
-  name: "res2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "bn2c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "scale2c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "bn2c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "scale2c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2c"
-  name: "res2c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "bn2c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "scale2c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b"
-  bottom: "res2c_branch2c"
-  top: "res2c"
-  name: "res2c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res2c"
-  name: "res2c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch1"
-  name: "res3a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "bn3a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "scale3a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "bn3a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "scale3a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "bn3a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "scale3a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2c"
-  name: "res3a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "bn3a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "scale3a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  bottom: "res3a_branch2c"
-  top: "res3a"
-  name: "res3a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3a"
-  name: "res3a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "bn3b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "scale3b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "bn3b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "scale3b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2c"
-  name: "res3b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "bn3b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "scale3b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a"
-  bottom: "res3b_branch2c"
-  top: "res3b"
-  name: "res3b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3b"
-  name: "res3b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "bn3c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "scale3c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "bn3c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "scale3c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2c"
-  name: "res3c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "bn3c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "scale3c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b"
-  bottom: "res3c_branch2c"
-  top: "res3c"
-  name: "res3c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3c"
-  name: "res3c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "bn3d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "scale3d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "bn3d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "scale3d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2c"
-  name: "res3d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "bn3d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "scale3d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c"
-  bottom: "res3d_branch2c"
-  top: "res3d"
-  name: "res3d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res3d"
-  name: "res3d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch1"
-  name: "res4a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "bn4a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "scale4a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "bn4a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "scale4a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "bn4a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "scale4a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2c"
-  name: "res4a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "bn4a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "scale4a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  bottom: "res4a_branch2c"
-  top: "res4a"
-  name: "res4a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4a"
-  name: "res4a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "bn4b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "scale4b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "bn4b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "scale4b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2c"
-  name: "res4b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "bn4b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "scale4b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a"
-  bottom: "res4b_branch2c"
-  top: "res4b"
-  name: "res4b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4b"
-  name: "res4b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "bn4c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "scale4c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "bn4c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "scale4c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2c"
-  name: "res4c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "bn4c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "scale4c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b"
-  bottom: "res4c_branch2c"
-  top: "res4c"
-  name: "res4c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4c"
-  name: "res4c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "bn4d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "scale4d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "bn4d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "scale4d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2c"
-  name: "res4d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "bn4d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "scale4d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c"
-  bottom: "res4d_branch2c"
-  top: "res4d"
-  name: "res4d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4d"
-  name: "res4d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "bn4e_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "scale4e_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "bn4e_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "scale4e_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2c"
-  name: "res4e_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "bn4e_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "scale4e_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d"
-  bottom: "res4e_branch2c"
-  top: "res4e"
-  name: "res4e"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4e"
-  name: "res4e_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "bn4f_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "scale4f_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "bn4f_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "scale4f_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2c"
-  name: "res4f_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "bn4f_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "scale4f_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e"
-  bottom: "res4f_branch2c"
-  top: "res4f"
-  name: "res4f"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res4f"
-  name: "res4f_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch1"
-  name: "res5a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "bn5a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "scale5a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "bn5a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "scale5a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "bn5a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "scale5a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2c"
-  name: "res5a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "bn5a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "scale5a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  bottom: "res5a_branch2c"
-  top: "res5a"
-  name: "res5a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5a"
-  name: "res5a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "bn5b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "scale5b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "bn5b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "scale5b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2c"
-  name: "res5b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "bn5b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "scale5b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a"
-  bottom: "res5b_branch2c"
-  top: "res5b"
-  name: "res5b"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5b"
-  name: "res5b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "bn5c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "scale5c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "bn5c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "scale5c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2c"
-  name: "res5c_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "bn5c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "scale5c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b"
-  bottom: "res5c_branch2c"
-  top: "res5c"
-  name: "res5c"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "res5c"
-  name: "res5c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "pool5"
-  name: "pool5"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 7
-    stride: 1
-    pool: AVE
-  }
-}
-
-layer {
-  bottom: "pool5"
-  top: "fc1000"
-  name: "fc1000"
-  type: "InnerProduct"
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss"
-  name: "prob"
-  type: "SoftmaxWithLoss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  accuracy_param {
-    top_k: 5
-  }
-}
+name: "ResNet-50"
+bn_stats_batch_size: 32
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    scale: 0.0078125
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+    random_aspect_ratio_param {
+      min_area_ratio: 0.08
+      max_area_ratio: 1
+      aspect_ratio_change: 0.75
+      resize_param {
+        interp_mode: CUBIC
+      }
+    }
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 128
+    backend: LMDB
+    prefetch: 2
+    shuffle: true
+  }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    scale: 0.0078125
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+    random_resize_param {
+      min_size: 256
+      max_size: 256
+      resize_param {
+        interp_mode: CUBIC
+      }
+    }
+  }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
+}
+
+layer {
+  bottom: "data"
+  top: "conv1"
+  name: "conv1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 7
+    pad: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+      variance_norm: FAN_OUT
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "bn_conv1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "scale_conv1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "conv1"
+  name: "conv1_relu"
+  type: "ReLU"
+  relu_param {
+  }
+}
+
+layer {
+  bottom: "conv1"
+  top: "pool1"
+  name: "pool1"
+  type: "Pooling"
+  pooling_param {
+    kernel_size: 3
+    stride: 2
+    pool: MAX
+  }
+}
+
+layer {
+  bottom: "pool1"
+  top: "res2a_branch1"
+  name: "res2a_branch1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  name: "bn2a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  top: "res2a_branch1"
+  name: "scale2a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "pool1"
+  top: "res2a_branch2a"
+  name: "res2a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "bn2a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "scale2a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2a"
+  name: "res2a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a_branch2a"
+  top: "res2a_branch2b"
+  name: "res2a_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "bn2a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "scale2a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2b"
+  name: "res2a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a_branch2b"
+  top: "res2a_branch2c"
+  name: "res2a_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2c"
+  top: "res2a_branch2c"
+  name: "bn2a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2a_branch2c"
+  top: "res2a_branch2c"
+  name: "scale2a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a_branch1"
+  bottom: "res2a_branch2c"
+  top: "res2a"
+  name: "res2a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a"
+  top: "res2a"
+  name: "res2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2a"
+  top: "res2b_branch2a"
+  name: "res2b_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "bn2b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "scale2b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2a"
+  name: "res2b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b_branch2a"
+  top: "res2b_branch2b"
+  name: "res2b_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "bn2b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "scale2b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2b"
+  name: "res2b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b_branch2b"
+  top: "res2b_branch2c"
+  name: "res2b_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2c"
+  top: "res2b_branch2c"
+  name: "bn2b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2b_branch2c"
+  top: "res2b_branch2c"
+  name: "scale2b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2a"
+  bottom: "res2b_branch2c"
+  top: "res2b"
+  name: "res2b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b"
+  top: "res2b"
+  name: "res2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2b"
+  top: "res2c_branch2a"
+  name: "res2c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 64
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "bn2c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "scale2c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2a"
+  name: "res2c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c_branch2a"
+  top: "res2c_branch2b"
+  name: "res2c_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "bn2c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "scale2c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2b"
+  name: "res2c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c_branch2b"
+  top: "res2c_branch2c"
+  name: "res2c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2c"
+  top: "res2c_branch2c"
+  name: "bn2c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res2c_branch2c"
+  top: "res2c_branch2c"
+  name: "scale2c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2b"
+  bottom: "res2c_branch2c"
+  top: "res2c"
+  name: "res2c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res2c"
+  name: "res2c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res3a_branch1"
+  name: "res3a_branch1"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  top: "res3a_branch1"
+  name: "bn3a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  top: "res3a_branch1"
+  name: "scale3a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res2c"
+  top: "res3a_branch2a"
+  name: "res3a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "bn3a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "scale3a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2a"
+  name: "res3a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a_branch2a"
+  top: "res3a_branch2b"
+  name: "res3a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "bn3a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "scale3a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2b"
+  name: "res3a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a_branch2b"
+  top: "res3a_branch2c"
+  name: "res3a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2c"
+  top: "res3a_branch2c"
+  name: "bn3a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3a_branch2c"
+  top: "res3a_branch2c"
+  name: "scale3a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a_branch1"
+  bottom: "res3a_branch2c"
+  top: "res3a"
+  name: "res3a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a"
+  top: "res3a"
+  name: "res3a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3a"
+  top: "res3b_branch2a"
+  name: "res3b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "bn3b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "scale3b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2a"
+  name: "res3b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b_branch2a"
+  top: "res3b_branch2b"
+  name: "res3b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "bn3b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "scale3b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2b"
+  name: "res3b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b_branch2b"
+  top: "res3b_branch2c"
+  name: "res3b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2c"
+  top: "res3b_branch2c"
+  name: "bn3b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3b_branch2c"
+  top: "res3b_branch2c"
+  name: "scale3b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3a"
+  bottom: "res3b_branch2c"
+  top: "res3b"
+  name: "res3b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b"
+  top: "res3b"
+  name: "res3b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3b"
+  top: "res3c_branch2a"
+  name: "res3c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "bn3c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "scale3c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2a"
+  name: "res3c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c_branch2a"
+  top: "res3c_branch2b"
+  name: "res3c_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "bn3c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "scale3c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2b"
+  name: "res3c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c_branch2b"
+  top: "res3c_branch2c"
+  name: "res3c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2c"
+  top: "res3c_branch2c"
+  name: "bn3c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3c_branch2c"
+  top: "res3c_branch2c"
+  name: "scale3c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3b"
+  bottom: "res3c_branch2c"
+  top: "res3c"
+  name: "res3c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c"
+  top: "res3c"
+  name: "res3c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3c"
+  top: "res3d_branch2a"
+  name: "res3d_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "bn3d_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "scale3d_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2a"
+  name: "res3d_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d_branch2a"
+  top: "res3d_branch2b"
+  name: "res3d_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 128
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "bn3d_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "scale3d_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2b"
+  name: "res3d_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d_branch2b"
+  top: "res3d_branch2c"
+  name: "res3d_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2c"
+  top: "res3d_branch2c"
+  name: "bn3d_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res3d_branch2c"
+  top: "res3d_branch2c"
+  name: "scale3d_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3c"
+  bottom: "res3d_branch2c"
+  top: "res3d"
+  name: "res3d"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res3d"
+  name: "res3d_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res4a_branch1"
+  name: "res4a_branch1"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  name: "bn4a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  top: "res4a_branch1"
+  name: "scale4a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res3d"
+  top: "res4a_branch2a"
+  name: "res4a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "bn4a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "scale4a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2a"
+  name: "res4a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a_branch2a"
+  top: "res4a_branch2b"
+  name: "res4a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "bn4a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "scale4a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2b"
+  name: "res4a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a_branch2b"
+  top: "res4a_branch2c"
+  name: "res4a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2c"
+  top: "res4a_branch2c"
+  name: "bn4a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4a_branch2c"
+  top: "res4a_branch2c"
+  name: "scale4a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a_branch1"
+  bottom: "res4a_branch2c"
+  top: "res4a"
+  name: "res4a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a"
+  top: "res4a"
+  name: "res4a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4a"
+  top: "res4b_branch2a"
+  name: "res4b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "bn4b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "scale4b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2a"
+  name: "res4b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b_branch2a"
+  top: "res4b_branch2b"
+  name: "res4b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "bn4b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "scale4b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2b"
+  name: "res4b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b_branch2b"
+  top: "res4b_branch2c"
+  name: "res4b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2c"
+  top: "res4b_branch2c"
+  name: "bn4b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4b_branch2c"
+  top: "res4b_branch2c"
+  name: "scale4b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4a"
+  bottom: "res4b_branch2c"
+  top: "res4b"
+  name: "res4b"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b"
+  top: "res4b"
+  name: "res4b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4b"
+  top: "res4c_branch2a"
+  name: "res4c_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "bn4c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "scale4c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2a"
+  name: "res4c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c_branch2a"
+  top: "res4c_branch2b"
+  name: "res4c_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "bn4c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "scale4c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2b"
+  name: "res4c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c_branch2b"
+  top: "res4c_branch2c"
+  name: "res4c_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2c"
+  top: "res4c_branch2c"
+  name: "bn4c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4c_branch2c"
+  top: "res4c_branch2c"
+  name: "scale4c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4b"
+  bottom: "res4c_branch2c"
+  top: "res4c"
+  name: "res4c"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c"
+  top: "res4c"
+  name: "res4c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4c"
+  top: "res4d_branch2a"
+  name: "res4d_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "bn4d_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "scale4d_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2a"
+  name: "res4d_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d_branch2a"
+  top: "res4d_branch2b"
+  name: "res4d_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "bn4d_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "scale4d_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2b"
+  name: "res4d_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d_branch2b"
+  top: "res4d_branch2c"
+  name: "res4d_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2c"
+  top: "res4d_branch2c"
+  name: "bn4d_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4d_branch2c"
+  top: "res4d_branch2c"
+  name: "scale4d_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4c"
+  bottom: "res4d_branch2c"
+  top: "res4d"
+  name: "res4d"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d"
+  top: "res4d"
+  name: "res4d_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4d"
+  top: "res4e_branch2a"
+  name: "res4e_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "bn4e_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "scale4e_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2a"
+  name: "res4e_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e_branch2a"
+  top: "res4e_branch2b"
+  name: "res4e_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "bn4e_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "scale4e_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2b"
+  name: "res4e_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e_branch2b"
+  top: "res4e_branch2c"
+  name: "res4e_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2c"
+  top: "res4e_branch2c"
+  name: "bn4e_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4e_branch2c"
+  top: "res4e_branch2c"
+  name: "scale4e_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4d"
+  bottom: "res4e_branch2c"
+  top: "res4e"
+  name: "res4e"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e"
+  top: "res4e"
+  name: "res4e_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4e"
+  top: "res4f_branch2a"
+  name: "res4f_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "bn4f_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "scale4f_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2a"
+  name: "res4f_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f_branch2a"
+  top: "res4f_branch2b"
+  name: "res4f_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "bn4f_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "scale4f_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2b"
+  name: "res4f_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f_branch2b"
+  top: "res4f_branch2c"
+  name: "res4f_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 1024
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2c"
+  top: "res4f_branch2c"
+  name: "bn4f_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res4f_branch2c"
+  top: "res4f_branch2c"
+  name: "scale4f_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4e"
+  bottom: "res4f_branch2c"
+  top: "res4f"
+  name: "res4f"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res4f"
+  name: "res4f_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res5a_branch1"
+  name: "res5a_branch1"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  top: "res5a_branch1"
+  name: "bn5a_branch1"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  top: "res5a_branch1"
+  name: "scale5a_branch1"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res4f"
+  top: "res5a_branch2a"
+  name: "res5a_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 2
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "bn5a_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "scale5a_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2a"
+  name: "res5a_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a_branch2a"
+  top: "res5a_branch2b"
+  name: "res5a_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "bn5a_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "scale5a_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2b"
+  name: "res5a_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a_branch2b"
+  top: "res5a_branch2c"
+  name: "res5a_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
+  name: "bn5a_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5a_branch2c"
+  top: "res5a_branch2c"
+  name: "scale5a_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a_branch1"
+  bottom: "res5a_branch2c"
+  top: "res5a"
+  name: "res5a"
+  type: "Eltwise"
+  eltwise_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a"
+  top: "res5a"
+  name: "res5a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5a"
+  top: "res5b_branch2a"
+  name: "res5b_branch2a"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "bn5b_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "scale5b_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2a"
+  name: "res5b_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b_branch2a"
+  top: "res5b_branch2b"
+  name: "res5b_branch2b"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "bn5b_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "scale5b_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2b"
+  name: "res5b_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b_branch2b"
+  top: "res5b_branch2c"
+  name: "res5b_branch2c"
+  type: "Convolution"
+  convolution_param {
+
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2c"
+  top: "res5b_branch2c"
+  name: "bn5b_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5b_branch2c"
+  top: "res5b_branch2c"
+  name: "scale5b_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5a"
+  bottom: "res5b_branch2c"
+  top: "res5b"
+  name: "res5b"
+  type: "Eltwise"
+  eltwise_param {
+  }
+}
+
+layer {
+  bottom: "res5b"
+  top: "res5b"
+  name: "res5b_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5b"
+  top: "res5c_branch2a"
+  name: "res5c_branch2a"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "bn5c_branch2a"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "scale5c_branch2a"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2a"
+  name: "res5c_branch2a_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5c_branch2a"
+  top: "res5c_branch2b"
+  name: "res5c_branch2b"
+  type: "Convolution"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "bn5c_branch2b"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+    filler { value: 1 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "scale5c_branch2b"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2b"
+  name: "res5c_branch2b_relu"
+  type: "ReLU"
+  relu_param {
+  }
+}
+
+layer {
+  bottom: "res5c_branch2b"
+  top: "res5c_branch2c"
+  name: "res5c_branch2c"
+  type: "Convolution"
+  convolution_param {
+    num_output: 2048
+    kernel_size: 1
+    pad: 0
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  name: "bn5c_branch2c"
+  type: "BatchNorm"
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  param { lr_mult: 0 }
+  batch_norm_param {
+    moving_average_fraction: 0.9
+  filler { value: 0 }
+  }
+}
+
+layer {
+  bottom: "res5c_branch2c"
+  top: "res5c_branch2c"
+  name: "scale5c_branch2c"
+  type: "Scale"
+  param { decay_mult: 0 }
+  param { decay_mult: 0 }
+  scale_param {
+    bias_term: true
+  }
+}
+
+layer {
+  bottom: "res5b"
+  bottom: "res5c_branch2c"
+  top: "res5c"
+  name: "res5c"
+  type: "Eltwise"
+  eltwise_param {
+  }
+}
+
+layer {
+  bottom: "res5c"
+  top: "res5c"
+  name: "res5c_relu"
+  type: "ReLU"
+  relu_param {
+
+  }
+}
+
+layer {
+  bottom: "res5c"
+  top: "pool5"
+  name: "pool5"
+  type: "Pooling"
+  pooling_param {
+    kernel_size: 7
+    stride: 1
+    pool: AVE
+  }
+}
+
+layer {
+  bottom: "pool5"
+  top: "fc1000"
+  name: "fc1000"
+  type: "InnerProduct"
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss"
+  name: "prob"
+  type: "SoftmaxWithLoss"
+  include {
+      phase: TRAIN
+  }
+}
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss3/top-1"
+}
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
+  bottom: "fc1000"
+  bottom: "label"
+  top: "loss3/top-5"
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/solver.prototxt b/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/solver.prototxt
new file mode 100644
index 000000000..e1d42882c
--- /dev/null
+++ b/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/solver.prototxt
@@ -0,0 +1,25 @@
+net: "models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/train_val.prototxt"
+
+warmup_iter: 3125
+warmup_start_lr: 0.05
+
+test_iter: 1000
+test_interval: 6250
+test_initialization: false
+
+display: 100
+
+max_iter: 62500        # 100 epochs
+base_lr: 0.6
+
+lr_policy: "poly"
+power: 2
+momentum: 0.9
+weight_decay: 0.0002
+
+snapshot: 6250
+snapshot_prefix: "models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/snapshots/googlenet_v3"
+
+random_seed: 1
+
+solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/train_val.prototxt b/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/train_val.prototxt
new file mode 100644
index 000000000..6ee6ebc5a
--- /dev/null
+++ b/models/intel_optimized_models/multinode/googlenet_v3_32nodes_2k_batch/train_val.prototxt
@@ -0,0 +1,3794 @@
+name: "InceptionV3"
+bn_stats_batch_size: 32
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    shuffle: true
+    backend: LMDB
+    batch_size: 64
+  }
+  transform_param {
+    crop_size: 299
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+
+    mirror: true
+
+    random_aspect_ratio_param {
+        min_area_ratio: 0.05
+        max_area_ratio: 1
+        aspect_ratio_change: 0.75
+        max_attempt: 100
+        resize_param {
+        interp_mode: CUBIC
+        }
+    }
+  }
+  include: { phase: TRAIN }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    shuffle: false
+    backend: LMDB
+    batch_size: 50
+  }
+  transform_param {
+    crop_size: 299
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+
+    mirror: false
+
+    random_resize_param {
+        min_size: 320
+        max_size: 320
+        resize_param {
+        interp_mode: CUBIC
+        }
+    }
+  }
+  include: { phase: TEST }
+}
+layer {
+  name: "conv_conv2d"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_conv2d"
+  top: "conv_conv2d_bn"
+
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_relu"
+  type: "ReLU"
+  bottom: "conv_conv2d_bn"
+  top: "conv_conv2d_relu"
+}
+layer {
+  name: "conv_1_1_conv2d"
+  type: "Convolution"
+  bottom: "conv_conv2d_relu"
+  top: "conv_1_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_1_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_1_1_conv2d"
+  top: "conv_1_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_1_1_relu"
+  type: "ReLU"
+  bottom: "conv_1_1_conv2d_bn"
+  top: "conv_1_1_conv2d_relu"
+}
+layer {
+  name: "conv_2_2_conv2d"
+  type: "Convolution"
+  bottom: "conv_1_1_conv2d_relu"
+  top: "conv_2_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_2_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_2_2_conv2d"
+  top: "conv_2_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_2_2_relu"
+  type: "ReLU"
+  bottom: "conv_2_2_conv2d_bn"
+  top: "conv_2_2_conv2d_relu"
+}
+layer {
+  name: "pool"
+  type: "Pooling"
+  bottom: "conv_2_2_conv2d_relu"
+  top: "pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv_3_3_conv2d"
+  type: "Convolution"
+  bottom: "pool"
+  top: "conv_3_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 80
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_3_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_3_3_conv2d"
+  top: "conv_3_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_3_3_relu"
+  type: "ReLU"
+  bottom: "conv_3_3_conv2d_bn"
+  top: "conv_3_3_conv2d_relu"
+}
+layer {
+  name: "conv_4_4_conv2d"
+  type: "Convolution"
+  bottom: "conv_3_3_conv2d_relu"
+  top: "conv_4_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_4_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_4_4_conv2d"
+  top: "conv_4_4_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_4_4_relu"
+  type: "ReLU"
+  bottom: "conv_4_4_conv2d_bn"
+  top: "conv_4_4_conv2d_relu"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv_4_4_conv2d_relu"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "mixed_conv_conv2d"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "mixed_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_conv_conv2d"
+  top: "mixed_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_conv_conv2d_bn"
+  top: "mixed_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "mixed_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 48
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_conv_conv2d"
+  top: "mixed_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_conv_conv2d_bn"
+  top: "mixed_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_tower_conv_conv2d_relu"
+  top: "mixed_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_conv_1_conv2d"
+  top: "mixed_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_conv_1_conv2d_bn"
+  top: "mixed_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "mixed_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_1_conv_conv2d"
+  top: "mixed_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_conv2d_bn"
+  top: "mixed_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_tower_1_conv_conv2d_relu"
+  top: "mixed_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_1_conv_1_conv2d"
+  top: "mixed_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_1_conv2d_bn"
+  top: "mixed_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_tower_1_conv_1_conv2d_relu"
+  top: "mixed_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_1_conv_2_conv2d"
+  top: "mixed_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_2_conv2d_bn"
+  top: "mixed_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_pool"
+  type: "Pooling"
+  bottom: "pool1"
+  top: "AVE_pool_mixed_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_pool"
+  top: "mixed_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_tower_2_conv_conv2d"
+  top: "mixed_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_tower_2_conv_conv2d_bn"
+  top: "mixed_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_chconcat"
+  type: "Concat"
+  bottom: "mixed_conv_conv2d_relu"
+  bottom: "mixed_tower_conv_1_conv2d_relu"
+  bottom: "mixed_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_conv_conv2d"
+  top: "mixed_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_1_conv_conv2d_bn"
+  top: "mixed_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 48
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_conv_conv2d"
+  top: "mixed_1_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_conv_conv2d_bn"
+  top: "mixed_1_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_1_tower_conv_conv2d_relu"
+  top: "mixed_1_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_conv_1_conv2d"
+  top: "mixed_1_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_conv_1_conv2d_bn"
+  top: "mixed_1_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_1_conv_conv2d"
+  top: "mixed_1_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_conv2d_bn"
+  top: "mixed_1_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_1_tower_1_conv_conv2d_relu"
+  top: "mixed_1_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_1_conv_1_conv2d"
+  top: "mixed_1_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_1_conv2d_bn"
+  top: "mixed_1_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_1_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_1_tower_1_conv_1_conv2d_relu"
+  top: "mixed_1_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_1_conv_2_conv2d"
+  top: "mixed_1_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_bn"
+  top: "mixed_1_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_1_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "AVE_pool_mixed_1_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_1_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_1_pool"
+  top: "mixed_1_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_1_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_1_tower_2_conv_conv2d"
+  top: "mixed_1_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_1_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_2_conv_conv2d_bn"
+  top: "mixed_1_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_1_chconcat"
+  type: "Concat"
+  bottom: "mixed_1_conv_conv2d_relu"
+  bottom: "mixed_1_tower_conv_1_conv2d_relu"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_1_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_1_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_conv_conv2d"
+  top: "mixed_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_conv_conv2d_bn"
+  top: "mixed_2_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 48
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_conv_conv2d"
+  top: "mixed_2_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_conv2d_bn"
+  top: "mixed_2_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_2_tower_conv_conv2d_relu"
+  top: "mixed_2_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_conv_1_conv2d"
+  top: "mixed_2_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_1_conv2d_bn"
+  top: "mixed_2_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_conv2d"
+  top: "mixed_2_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_conv2d_bn"
+  top: "mixed_2_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_2_tower_1_conv_conv2d_relu"
+  top: "mixed_2_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_1_conv2d"
+  top: "mixed_2_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_1_conv2d_bn"
+  top: "mixed_2_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_2_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_2_tower_1_conv_1_conv2d_relu"
+  top: "mixed_2_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_1_conv_2_conv2d"
+  top: "mixed_2_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_2_conv2d_bn"
+  top: "mixed_2_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_2_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "AVE_pool_mixed_2_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_2_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_2_pool"
+  top: "mixed_2_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_2_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_2_tower_2_conv_conv2d"
+  top: "mixed_2_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_2_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_2_tower_2_conv_conv2d_bn"
+  top: "mixed_2_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_2_chconcat"
+  type: "Concat"
+  bottom: "mixed_2_conv_conv2d_relu"
+  bottom: "mixed_2_tower_conv_1_conv2d_relu"
+  bottom: "mixed_2_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_2_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_2_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_3_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "mixed_3_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_3_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_3_conv_conv2d"
+  top: "mixed_3_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_3_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_3_conv_conv2d_bn"
+  top: "mixed_3_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_3_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "mixed_3_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_3_tower_conv_conv2d"
+  top: "mixed_3_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_3_tower_conv_conv2d_bn"
+  top: "mixed_3_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_3_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_3_tower_conv_conv2d_relu"
+  top: "mixed_3_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_3_tower_conv_1_conv2d"
+  top: "mixed_3_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_3_tower_conv_1_conv2d_bn"
+  top: "mixed_3_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_3_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_3_tower_conv_1_conv2d_relu"
+  top: "mixed_3_tower_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 96
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_3_tower_conv_2_conv2d"
+  top: "mixed_3_tower_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_3_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_3_tower_conv_2_conv2d_bn"
+  top: "mixed_3_tower_conv_2_conv2d_relu"
+}
+layer {
+  name: "max_pool_mixed_3_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "max_pool_mixed_3_pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "ch_concat_mixed_3_chconcat"
+  type: "Concat"
+  bottom: "max_pool_mixed_3_pool"
+  bottom: "mixed_3_conv_conv2d_relu"
+  bottom: "mixed_3_tower_conv_2_conv2d_relu"
+  top: "ch_concat_mixed_3_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_4_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "mixed_4_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_4_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_conv_conv2d"
+  top: "mixed_4_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_4_conv_conv2d_bn"
+  top: "mixed_4_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "mixed_4_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_conv_conv2d"
+  top: "mixed_4_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_conv_conv2d_bn"
+  top: "mixed_4_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_conv_conv2d_relu"
+  top: "mixed_4_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_conv_1_conv2d"
+  top: "mixed_4_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_conv_1_conv2d_bn"
+  top: "mixed_4_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_conv_1_conv2d_relu"
+  top: "mixed_4_tower_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_conv_2_conv2d"
+  top: "mixed_4_tower_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_conv_2_conv2d_bn"
+  top: "mixed_4_tower_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "mixed_4_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_1_conv_conv2d"
+  top: "mixed_4_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_1_conv_conv2d_bn"
+  top: "mixed_4_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_1_conv_conv2d_relu"
+  top: "mixed_4_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_1_conv_1_conv2d"
+  top: "mixed_4_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_1_conv_1_conv2d_bn"
+  top: "mixed_4_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_1_conv_1_conv2d_relu"
+  top: "mixed_4_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_1_conv_2_conv2d"
+  top: "mixed_4_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_1_conv_2_conv2d_bn"
+  top: "mixed_4_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_1_conv_3_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_1_conv_2_conv2d_relu"
+  top: "mixed_4_tower_1_conv_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_1_conv_3_conv2d"
+  top: "mixed_4_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_1_conv_3_conv2d_bn"
+  top: "mixed_4_tower_1_conv_3_conv2d_relu"
+}
+layer {
+  name: "mixed_4_tower_1_conv_4_conv2d"
+  type: "Convolution"
+  bottom: "mixed_4_tower_1_conv_3_conv2d_relu"
+  top: "mixed_4_tower_1_conv_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_1_conv_4_conv2d"
+  top: "mixed_4_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_1_conv_4_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_1_conv_4_conv2d_bn"
+  top: "mixed_4_tower_1_conv_4_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_4_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "AVE_pool_mixed_4_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_4_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_4_pool"
+  top: "mixed_4_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_4_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_4_tower_2_conv_conv2d"
+  top: "mixed_4_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_4_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_4_tower_2_conv_conv2d_bn"
+  top: "mixed_4_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_4_chconcat"
+  type: "Concat"
+  bottom: "mixed_4_conv_conv2d_relu"
+  bottom: "mixed_4_tower_conv_2_conv2d_relu"
+  bottom: "mixed_4_tower_1_conv_4_conv2d_relu"
+  bottom: "mixed_4_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_4_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_5_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_4_chconcat"
+  top: "mixed_5_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_5_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_conv_conv2d"
+  top: "mixed_5_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_5_conv_conv2d_bn"
+  top: "mixed_5_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_4_chconcat"
+  top: "mixed_5_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_conv_conv2d"
+  top: "mixed_5_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_conv_conv2d_bn"
+  top: "mixed_5_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_conv_conv2d_relu"
+  top: "mixed_5_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_conv_1_conv2d"
+  top: "mixed_5_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_conv_1_conv2d_bn"
+  top: "mixed_5_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_conv_1_conv2d_relu"
+  top: "mixed_5_tower_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_conv_2_conv2d"
+  top: "mixed_5_tower_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_conv_2_conv2d_bn"
+  top: "mixed_5_tower_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_4_chconcat"
+  top: "mixed_5_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_conv2d"
+  top: "mixed_5_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_conv2d_bn"
+  top: "mixed_5_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_conv2d_relu"
+  top: "mixed_5_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_1_conv2d"
+  top: "mixed_5_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_1_conv2d_bn"
+  top: "mixed_5_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_1_conv2d_relu"
+  top: "mixed_5_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_2_conv2d"
+  top: "mixed_5_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_2_conv2d_bn"
+  top: "mixed_5_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_1_conv_3_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_2_conv2d_relu"
+  top: "mixed_5_tower_1_conv_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_3_conv2d"
+  top: "mixed_5_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_3_conv2d_bn"
+  top: "mixed_5_tower_1_conv_3_conv2d_relu"
+}
+layer {
+  name: "mixed_5_tower_1_conv_4_conv2d"
+  type: "Convolution"
+  bottom: "mixed_5_tower_1_conv_3_conv2d_relu"
+  top: "mixed_5_tower_1_conv_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_1_conv_4_conv2d"
+  top: "mixed_5_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_1_conv_4_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_4_conv2d_bn"
+  top: "mixed_5_tower_1_conv_4_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_5_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_4_chconcat"
+  top: "AVE_pool_mixed_5_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_5_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_5_pool"
+  top: "mixed_5_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_5_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_5_tower_2_conv_conv2d"
+  top: "mixed_5_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_5_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_5_tower_2_conv_conv2d_bn"
+  top: "mixed_5_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_5_chconcat"
+  type: "Concat"
+  bottom: "mixed_5_conv_conv2d_relu"
+  bottom: "mixed_5_tower_conv_2_conv2d_relu"
+  bottom: "mixed_5_tower_1_conv_4_conv2d_relu"
+  bottom: "mixed_5_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_5_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_6_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "mixed_6_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_6_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_conv_conv2d"
+  top: "mixed_6_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_6_conv_conv2d_bn"
+  top: "mixed_6_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "mixed_6_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_conv_conv2d"
+  top: "mixed_6_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_conv_conv2d_bn"
+  top: "mixed_6_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_conv_conv2d_relu"
+  top: "mixed_6_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_conv_1_conv2d"
+  top: "mixed_6_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_conv_1_conv2d_bn"
+  top: "mixed_6_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_conv_1_conv2d_relu"
+  top: "mixed_6_tower_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_conv_2_conv2d"
+  top: "mixed_6_tower_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_conv_2_conv2d_bn"
+  top: "mixed_6_tower_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "mixed_6_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_1_conv_conv2d"
+  top: "mixed_6_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_1_conv_conv2d_bn"
+  top: "mixed_6_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_1_conv_conv2d_relu"
+  top: "mixed_6_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_1_conv_1_conv2d"
+  top: "mixed_6_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_1_conv_1_conv2d_bn"
+  top: "mixed_6_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_1_conv_1_conv2d_relu"
+  top: "mixed_6_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_1_conv_2_conv2d"
+  top: "mixed_6_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_1_conv_2_conv2d_bn"
+  top: "mixed_6_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_1_conv_3_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_1_conv_2_conv2d_relu"
+  top: "mixed_6_tower_1_conv_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 160
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_1_conv_3_conv2d"
+  top: "mixed_6_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_1_conv_3_conv2d_bn"
+  top: "mixed_6_tower_1_conv_3_conv2d_relu"
+}
+layer {
+  name: "mixed_6_tower_1_conv_4_conv2d"
+  type: "Convolution"
+  bottom: "mixed_6_tower_1_conv_3_conv2d_relu"
+  top: "mixed_6_tower_1_conv_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_1_conv_4_conv2d"
+  top: "mixed_6_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_1_conv_4_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_1_conv_4_conv2d_bn"
+  top: "mixed_6_tower_1_conv_4_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_6_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "AVE_pool_mixed_6_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_6_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_6_pool"
+  top: "mixed_6_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_6_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_6_tower_2_conv_conv2d"
+  top: "mixed_6_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_6_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_6_tower_2_conv_conv2d_bn"
+  top: "mixed_6_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_6_chconcat"
+  type: "Concat"
+  bottom: "mixed_6_conv_conv2d_relu"
+  bottom: "mixed_6_tower_conv_2_conv2d_relu"
+  bottom: "mixed_6_tower_1_conv_4_conv2d_relu"
+  bottom: "mixed_6_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_6_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_7_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "mixed_7_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_7_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_conv_conv2d"
+  top: "mixed_7_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_7_conv_conv2d_bn"
+  top: "mixed_7_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "mixed_7_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_conv_conv2d"
+  top: "mixed_7_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_conv_conv2d_bn"
+  top: "mixed_7_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_conv_conv2d_relu"
+  top: "mixed_7_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_conv_1_conv2d"
+  top: "mixed_7_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_conv_1_conv2d_bn"
+  top: "mixed_7_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_conv_1_conv2d_relu"
+  top: "mixed_7_tower_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_conv_2_conv2d"
+  top: "mixed_7_tower_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_conv_2_conv2d_bn"
+  top: "mixed_7_tower_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "mixed_7_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_conv2d"
+  top: "mixed_7_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_conv2d_bn"
+  top: "mixed_7_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_conv2d_relu"
+  top: "mixed_7_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_1_conv2d"
+  top: "mixed_7_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_1_conv2d_bn"
+  top: "mixed_7_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_1_conv2d_relu"
+  top: "mixed_7_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_2_conv2d"
+  top: "mixed_7_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_2_conv2d_bn"
+  top: "mixed_7_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_1_conv_3_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_2_conv2d_relu"
+  top: "mixed_7_tower_1_conv_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_3_conv2d"
+  top: "mixed_7_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_3_conv2d_bn"
+  top: "mixed_7_tower_1_conv_3_conv2d_relu"
+}
+layer {
+  name: "mixed_7_tower_1_conv_4_conv2d"
+  type: "Convolution"
+  bottom: "mixed_7_tower_1_conv_3_conv2d_relu"
+  top: "mixed_7_tower_1_conv_4_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_4_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_1_conv_4_conv2d"
+  top: "mixed_7_tower_1_conv_4_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_1_conv_4_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_1_conv_4_conv2d_bn"
+  top: "mixed_7_tower_1_conv_4_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_7_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_6_chconcat"
+  top: "AVE_pool_mixed_7_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_7_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_7_pool"
+  top: "mixed_7_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_7_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_7_tower_2_conv_conv2d"
+  top: "mixed_7_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_7_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_7_tower_2_conv_conv2d_bn"
+  top: "mixed_7_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_7_chconcat"
+  type: "Concat"
+  bottom: "mixed_7_conv_conv2d_relu"
+  bottom: "mixed_7_tower_conv_2_conv2d_relu"
+  bottom: "mixed_7_tower_1_conv_4_conv2d_relu"
+  bottom: "mixed_7_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_7_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_8_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_7_chconcat"
+  top: "mixed_8_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_8_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_conv_conv2d"
+  top: "mixed_8_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_conv_conv2d_bn"
+  top: "mixed_8_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_8_tower_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_8_tower_conv_conv2d_relu"
+  top: "mixed_8_tower_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 320
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_8_tower_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_conv_1_conv2d"
+  top: "mixed_8_tower_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_conv_1_conv2d_bn"
+  top: "mixed_8_tower_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_8_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_7_chconcat"
+  top: "mixed_8_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_1_conv_conv2d"
+  top: "mixed_8_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_1_conv_conv2d_bn"
+  top: "mixed_8_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_8_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_8_tower_1_conv_conv2d_relu"
+  top: "mixed_8_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 3
+    kernel_h: 1
+    kernel_w: 7
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_1_conv_1_conv2d"
+  top: "mixed_8_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_1_conv_1_conv2d_bn"
+  top: "mixed_8_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_8_tower_1_conv_2_conv2d"
+  type: "Convolution"
+  bottom: "mixed_8_tower_1_conv_1_conv2d_relu"
+  top: "mixed_8_tower_1_conv_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 3
+    pad_w: 0
+    kernel_h: 7
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_2_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_1_conv_2_conv2d"
+  top: "mixed_8_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_1_conv_2_conv2d_bn"
+  top: "mixed_8_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "mixed_8_tower_1_conv_3_conv2d"
+  type: "Convolution"
+  bottom: "mixed_8_tower_1_conv_2_conv2d_relu"
+  top: "mixed_8_tower_1_conv_3_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_1_conv_3_conv2d"
+  top: "mixed_8_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_8_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_1_conv_3_conv2d_bn"
+  top: "mixed_8_tower_1_conv_3_conv2d_relu"
+}
+layer {
+  name: "MAX_pool_mixed_8_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_7_chconcat"
+  top: "MAX_pool_mixed_8_pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "ch_concat_mixed_8_chconcat"
+  type: "Concat"
+  bottom: "mixed_8_tower_conv_1_conv2d_relu"
+  bottom: "mixed_8_tower_1_conv_3_conv2d_relu"
+  bottom: "MAX_pool_mixed_8_pool"
+  top: "ch_concat_mixed_8_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_9_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 320
+    bias_term: false
+    pad: 0
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    kernel_h: 1
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_9_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_conv_conv2d"
+  top: "mixed_9_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_conv_conv2d_bn"
+  top: "mixed_9_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_9_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_conv_conv2d"
+  top: "mixed_9_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_conv_conv2d_bn"
+  top: "mixed_9_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_conv2d"
+  type: "Convolution"
+  bottom: "mixed_9_tower_conv_conv2d_relu"
+  top: "mixed_9_tower_mixed_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 1
+    kernel_h: 1
+    kernel_w: 3
+  }
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_mixed_conv_conv2d"
+  top: "mixed_9_tower_mixed_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_mixed_conv_conv2d_bn"
+  top: "mixed_9_tower_mixed_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_9_tower_conv_conv2d_relu"
+  top: "mixed_9_tower_mixed_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 1
+    pad_w: 0
+    kernel_h: 3
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_mixed_conv_1_conv2d"
+  top: "mixed_9_tower_mixed_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_mixed_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn"
+  top: "mixed_9_tower_mixed_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 448
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_9_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_1_conv_conv2d"
+  top: "mixed_9_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_1_conv_conv2d_bn"
+  top: "mixed_9_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_9_tower_1_conv_conv2d_relu"
+  top: "mixed_9_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_9_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_1_conv_1_conv2d"
+  top: "mixed_9_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_1_conv_1_conv2d_bn"
+  top: "mixed_9_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_conv2d"
+  type: "Convolution"
+  bottom: "mixed_9_tower_1_conv_1_conv2d_relu"
+  top: "mixed_9_tower_1_mixed_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 1
+    kernel_h: 1
+    kernel_w: 3
+  }
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_1_mixed_conv_conv2d"
+  top: "mixed_9_tower_1_mixed_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_1_mixed_conv_conv2d_bn"
+  top: "mixed_9_tower_1_mixed_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_9_tower_1_conv_1_conv2d_relu"
+  top: "mixed_9_tower_1_mixed_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 1
+    pad_w: 0
+    kernel_h: 3
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d"
+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_1_mixed_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_9_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "AVE_pool_mixed_9_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_9_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "AVE_pool_mixed_9_pool"
+  top: "mixed_9_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_9_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_9_tower_2_conv_conv2d"
+  top: "mixed_9_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_9_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_9_tower_2_conv_conv2d_bn"
+  top: "mixed_9_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_9_chconcat"
+  type: "Concat"
+  bottom: "mixed_9_conv_conv2d_relu"
+  bottom: "mixed_9_tower_mixed_conv_conv2d_relu"
+  bottom: "mixed_9_tower_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_9_tower_1_mixed_conv_conv2d_relu"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_9_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_9_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mixed_10_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "mixed_10_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 320
+    bias_term: false
+    pad: 0
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    kernel_h: 1
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_10_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_conv_conv2d"
+  top: "mixed_10_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_conv_conv2d_bn"
+  top: "mixed_10_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "mixed_10_tower_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_10_tower_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_conv_conv2d"
+  top: "mixed_10_tower_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_conv_conv2d_bn"
+  top: "mixed_10_tower_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_conv2d"
+  type: "Convolution"
+  bottom: "mixed_10_tower_conv_conv2d_relu"
+  top: "mixed_10_tower_mixed_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 1
+    kernel_h: 1
+    kernel_w: 3
+  }
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_mixed_conv_conv2d"
+  top: "mixed_10_tower_mixed_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_mixed_conv_conv2d_bn"
+  top: "mixed_10_tower_mixed_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_10_tower_conv_conv2d_relu"
+  top: "mixed_10_tower_mixed_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 1
+    pad_w: 0
+    kernel_h: 3
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_mixed_conv_1_conv2d"
+  top: "mixed_10_tower_mixed_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_mixed_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_mixed_conv_1_conv2d_bn"
+  top: "mixed_10_tower_mixed_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "mixed_10_tower_1_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 448
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_10_tower_1_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_1_conv_conv2d"
+  top: "mixed_10_tower_1_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_1_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_1_conv_conv2d_bn"
+  top: "mixed_10_tower_1_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_1_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_10_tower_1_conv_conv2d_relu"
+  top: "mixed_10_tower_1_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_10_tower_1_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_1_conv_1_conv2d"
+  top: "mixed_10_tower_1_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_1_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_1_conv_1_conv2d_bn"
+  top: "mixed_10_tower_1_conv_1_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_conv2d"
+  type: "Convolution"
+  bottom: "mixed_10_tower_1_conv_1_conv2d_relu"
+  top: "mixed_10_tower_1_mixed_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 0
+    pad_w: 1
+    kernel_h: 1
+    kernel_w: 3
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d"
+  top: "mixed_10_tower_1_mixed_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn"
+  top: "mixed_10_tower_1_mixed_conv_conv2d_relu"
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_1_conv2d"
+  type: "Convolution"
+  bottom: "mixed_10_tower_1_conv_1_conv2d_relu"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 384
+    bias_term: false
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    pad_h: 1
+    pad_w: 0
+    kernel_h: 3
+    kernel_w: 1
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
+}
+layer {
+  name: "MAX_pool_mixed_10_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "MAX_pool_mixed_10_pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "MAX_pool_mixed_10_pool"
+  top: "mixed_10_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_2_conv_conv2d"
+  top: "mixed_10_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_2_conv_conv2d_bn"
+  top: "mixed_10_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_10_chconcat"
+  type: "Concat"
+  bottom: "mixed_10_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_10_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "global_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_10_chconcat"
+  top: "global_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 8
+    stride: 1
+    pad: 0
+  }
+}
+layer {
+  name: "drop"
+  type: "Dropout"
+  bottom: "global_pool"
+  top: "global_pool"
+  dropout_param {
+    dropout_ratio: 0.8
+  }
+}
+layer {
+  name: "flatten"
+  type: "Flatten"
+  bottom: "global_pool"
+  top: "flatten"
+}
+layer {
+  name: "fc1"
+  type: "InnerProduct"
+  bottom: "flatten"
+  top: "fc1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc1"
+  bottom: "label"
+  top: "loss"
+}
+layer {
+  name: "acc/top-1"
+  type: "Accuracy"
+  bottom: "fc1"
+  bottom: "label"
+  top: "acc/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "acc/top-5"
+  type: "Accuracy"
+  bottom: "fc1"
+  bottom: "label"
+  top: "acc/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/solver.prototxt b/models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/solver.prototxt
deleted file mode 100644
index 2356cc00f..000000000
--- a/models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/solver.prototxt
+++ /dev/null
@@ -1,19 +0,0 @@
-net: "models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/train_val.prototxt"
-test_iter: 1000
-test_interval: 625
-test_initialization: false
-display: 40
-base_lr: 0.8
-lr_policy: "multistep"
-stepvalue:18750
-stepvalue:37500
-stepvalue:50000
-gamma: 0.1
-max_iter: 56300
-warmup_iter: 3125 # 1281167 / 2048 * 5 epochs
-warmup_start_lr: 0.1
-momentum: 0.9
-weight_decay: 0.0001
-snapshot: 6250
-snapshot_prefix: "models/intel_optimized_models/multinode/resnet_50_16_nodes_2k_batch/resnet_50_16_nodes_2k"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/solver.prototxt b/models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/solver.prototxt
deleted file mode 100644
index 8f03f6a3a..000000000
--- a/models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/solver.prototxt
+++ /dev/null
@@ -1,19 +0,0 @@
-net: "models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/train_val.prototxt"
-test_iter: 1000
-test_interval: 156
-test_initialization: false
-display: 40
-base_lr: 3.2
-lr_policy: "multistep"
-stepvalue:4680
-stepvalue:9360
-stepvalue:12480
-gamma: 0.1
-max_iter: 14075
-warmup_iter: 780 # 1281167 / 8192 * 5 epochs
-warmup_start_lr: 0.1
-momentum: 0.9
-weight_decay: 0.0001
-snapshot: 156
-snapshot_prefix: "models/intel_optimized_models/multinode/resnet_50_256_nodes_8k_batch/resnet_50_256_nodes_8k"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/multinode/resnet_50_8_nodes/solver.prototxt b/models/intel_optimized_models/multinode/resnet_50_8_nodes/solver.prototxt
deleted file mode 100644
index 182626503..000000000
--- a/models/intel_optimized_models/multinode/resnet_50_8_nodes/solver.prototxt
+++ /dev/null
@@ -1,17 +0,0 @@
-net: "models/intel_optimized_models/multinode/resnet_50_8_nodes/train_val.prototxt"
-test_iter: 1000
-test_interval: 1248
-test_initialization: false
-display: 40
-base_lr: 0.4
-lr_policy: "multistep"
-stepvalue:37440
-stepvalue:74880
-stepvalue:99840
-gamma: 0.1
-max_iter: 112600
-momentum: 0.9
-weight_decay: 0.0001
-snapshot: 12480
-snapshot_prefix: "models/intel_optimized_models/multinode/resnet_50_8_nodes/resnet_50_8_nodes"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/bdw/solver_dummydata.prototxt b/models/intel_optimized_models/resnet_50/bdw/solver_dummydata.prototxt
deleted file mode 100644
index 07c4dac00..000000000
--- a/models/intel_optimized_models/resnet_50/bdw/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/resnet_50/bdw/resnet_50_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt
deleted file mode 100644
index ba298f468..000000000
--- a/models/intel_optimized_models/resnet_50/bdw/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,3051 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 32  }
-    data_filler {
-      type: "constant"
-    }
-  }
-}
-
-layer {
-	bottom: "data"
-	top: "conv1"
-	name: "conv1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 7
-		pad: 3
-		stride: 2
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "bn_conv1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "scale_conv1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "conv1_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "conv1"
-	top: "pool1"
-	name: "pool1"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 3
-		stride: 2
-		pool: MAX
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch1"
-	name: "res2a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "bn2a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "scale2a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "bn2a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "scale2a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "bn2a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "scale2a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2c"
-	name: "res2a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "bn2a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "scale2a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	bottom: "res2a_branch2c"
-	top: "res2a"
-	name: "res2a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2a"
-	name: "res2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "bn2b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "scale2b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "bn2b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "scale2b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2c"
-	name: "res2b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "bn2b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "scale2b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a"
-	bottom: "res2b_branch2c"
-	top: "res2b"
-	name: "res2b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2b"
-	name: "res2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "bn2c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "scale2c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "bn2c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "scale2c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2c"
-	name: "res2c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "bn2c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "scale2c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b"
-	bottom: "res2c_branch2c"
-	top: "res2c"
-	name: "res2c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res2c"
-	name: "res2c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch1"
-	name: "res3a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "bn3a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "scale3a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "bn3a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "scale3a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "bn3a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "scale3a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2c"
-	name: "res3a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "bn3a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "scale3a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	bottom: "res3a_branch2c"
-	top: "res3a"
-	name: "res3a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3a"
-	name: "res3a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "bn3b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "scale3b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "bn3b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "scale3b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2c"
-	name: "res3b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "bn3b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "scale3b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a"
-	bottom: "res3b_branch2c"
-	top: "res3b"
-	name: "res3b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3b"
-	name: "res3b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "bn3c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "scale3c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "bn3c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "scale3c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2c"
-	name: "res3c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "bn3c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "scale3c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b"
-	bottom: "res3c_branch2c"
-	top: "res3c"
-	name: "res3c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3c"
-	name: "res3c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "bn3d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "scale3d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "bn3d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "scale3d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2c"
-	name: "res3d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "bn3d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "scale3d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c"
-	bottom: "res3d_branch2c"
-	top: "res3d"
-	name: "res3d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res3d"
-	name: "res3d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch1"
-	name: "res4a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "bn4a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "scale4a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "bn4a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "scale4a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "bn4a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "scale4a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2c"
-	name: "res4a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "bn4a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "scale4a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	bottom: "res4a_branch2c"
-	top: "res4a"
-	name: "res4a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4a"
-	name: "res4a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "bn4b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "scale4b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "bn4b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "scale4b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2c"
-	name: "res4b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "bn4b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "scale4b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a"
-	bottom: "res4b_branch2c"
-	top: "res4b"
-	name: "res4b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4b"
-	name: "res4b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "bn4c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "scale4c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "bn4c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "scale4c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2c"
-	name: "res4c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "bn4c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "scale4c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b"
-	bottom: "res4c_branch2c"
-	top: "res4c"
-	name: "res4c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4c"
-	name: "res4c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "bn4d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "scale4d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "bn4d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "scale4d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2c"
-	name: "res4d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "bn4d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "scale4d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c"
-	bottom: "res4d_branch2c"
-	top: "res4d"
-	name: "res4d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4d"
-	name: "res4d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "bn4e_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "scale4e_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "bn4e_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "scale4e_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2c"
-	name: "res4e_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "bn4e_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "scale4e_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d"
-	bottom: "res4e_branch2c"
-	top: "res4e"
-	name: "res4e"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4e"
-	name: "res4e_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "bn4f_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "scale4f_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "bn4f_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "scale4f_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2c"
-	name: "res4f_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "bn4f_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "scale4f_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e"
-	bottom: "res4f_branch2c"
-	top: "res4f"
-	name: "res4f"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res4f"
-	name: "res4f_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch1"
-	name: "res5a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "bn5a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "scale5a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "bn5a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "scale5a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "bn5a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "scale5a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2c"
-	name: "res5a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "bn5a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "scale5a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	bottom: "res5a_branch2c"
-	top: "res5a"
-	name: "res5a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5a"
-	name: "res5a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "bn5b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "scale5b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "bn5b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "scale5b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2c"
-	name: "res5b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "bn5b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "scale5b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a"
-	bottom: "res5b_branch2c"
-	top: "res5b"
-	name: "res5b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5b"
-	name: "res5b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "bn5c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "scale5c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "bn5c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "scale5c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2c"
-	name: "res5c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "bn5c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "scale5c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b"
-	bottom: "res5c_branch2c"
-	top: "res5c"
-	name: "res5c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "res5c"
-	name: "res5c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "pool5"
-	name: "pool5"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 7
-		stride: 1
-		pool: AVE
-	}
-}
-
-layer {
-	bottom: "pool5"
-	top: "fc1000"
-	name: "fc1000"
-	type: "InnerProduct"
-	inner_product_param {
-		num_output: 1000
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0
-    	        }
-	}
-}
-
-layer {
-	bottom: "fc1000"
-	bottom: "label"
-	top: "prob"
-	name: "prob"
-	type: "SoftmaxWithLoss"
-	include {
-	  phase: TRAIN
-	}
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/resnet_50/knl/solver_dummydata.prototxt b/models/intel_optimized_models/resnet_50/knl/solver_dummydata.prototxt
deleted file mode 100644
index 4dd554e8c..000000000
--- a/models/intel_optimized_models/resnet_50/knl/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/resnet_50/knl/resnet_50_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt
deleted file mode 100644
index 00c35771f..000000000
--- a/models/intel_optimized_models/resnet_50/knl/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,3051 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64  }
-    data_filler {
-      type: "constant"
-    }
-  }
-}
-
-layer {
-	bottom: "data"
-	top: "conv1"
-	name: "conv1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 7
-		pad: 3
-		stride: 2
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "bn_conv1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "scale_conv1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "conv1_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "conv1"
-	top: "pool1"
-	name: "pool1"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 3
-		stride: 2
-		pool: MAX
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch1"
-	name: "res2a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "bn2a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "scale2a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "bn2a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "scale2a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "bn2a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "scale2a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2c"
-	name: "res2a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "bn2a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "scale2a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	bottom: "res2a_branch2c"
-	top: "res2a"
-	name: "res2a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2a"
-	name: "res2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "bn2b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "scale2b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "bn2b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "scale2b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2c"
-	name: "res2b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "bn2b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "scale2b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a"
-	bottom: "res2b_branch2c"
-	top: "res2b"
-	name: "res2b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2b"
-	name: "res2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "bn2c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "scale2c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "bn2c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "scale2c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2c"
-	name: "res2c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "bn2c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "scale2c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b"
-	bottom: "res2c_branch2c"
-	top: "res2c"
-	name: "res2c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res2c"
-	name: "res2c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch1"
-	name: "res3a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "bn3a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "scale3a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "bn3a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "scale3a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "bn3a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "scale3a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2c"
-	name: "res3a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "bn3a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "scale3a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	bottom: "res3a_branch2c"
-	top: "res3a"
-	name: "res3a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3a"
-	name: "res3a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "bn3b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "scale3b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "bn3b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "scale3b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2c"
-	name: "res3b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "bn3b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "scale3b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a"
-	bottom: "res3b_branch2c"
-	top: "res3b"
-	name: "res3b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3b"
-	name: "res3b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "bn3c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "scale3c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "bn3c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "scale3c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2c"
-	name: "res3c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "bn3c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "scale3c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b"
-	bottom: "res3c_branch2c"
-	top: "res3c"
-	name: "res3c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3c"
-	name: "res3c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "bn3d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "scale3d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "bn3d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "scale3d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2c"
-	name: "res3d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "bn3d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "scale3d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c"
-	bottom: "res3d_branch2c"
-	top: "res3d"
-	name: "res3d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res3d"
-	name: "res3d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch1"
-	name: "res4a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "bn4a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "scale4a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "bn4a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "scale4a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "bn4a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "scale4a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2c"
-	name: "res4a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "bn4a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "scale4a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	bottom: "res4a_branch2c"
-	top: "res4a"
-	name: "res4a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4a"
-	name: "res4a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "bn4b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "scale4b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "bn4b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "scale4b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2c"
-	name: "res4b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "bn4b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "scale4b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a"
-	bottom: "res4b_branch2c"
-	top: "res4b"
-	name: "res4b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4b"
-	name: "res4b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "bn4c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "scale4c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "bn4c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "scale4c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2c"
-	name: "res4c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "bn4c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "scale4c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b"
-	bottom: "res4c_branch2c"
-	top: "res4c"
-	name: "res4c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4c"
-	name: "res4c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "bn4d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "scale4d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "bn4d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "scale4d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2c"
-	name: "res4d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "bn4d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "scale4d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c"
-	bottom: "res4d_branch2c"
-	top: "res4d"
-	name: "res4d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4d"
-	name: "res4d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "bn4e_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "scale4e_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "bn4e_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "scale4e_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2c"
-	name: "res4e_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "bn4e_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "scale4e_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d"
-	bottom: "res4e_branch2c"
-	top: "res4e"
-	name: "res4e"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4e"
-	name: "res4e_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "bn4f_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "scale4f_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "bn4f_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "scale4f_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2c"
-	name: "res4f_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "bn4f_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "scale4f_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e"
-	bottom: "res4f_branch2c"
-	top: "res4f"
-	name: "res4f"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res4f"
-	name: "res4f_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch1"
-	name: "res5a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "bn5a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "scale5a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "bn5a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "scale5a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "bn5a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "scale5a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2c"
-	name: "res5a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "bn5a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "scale5a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	bottom: "res5a_branch2c"
-	top: "res5a"
-	name: "res5a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5a"
-	name: "res5a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "bn5b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "scale5b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "bn5b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "scale5b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2c"
-	name: "res5b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "bn5b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "scale5b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a"
-	bottom: "res5b_branch2c"
-	top: "res5b"
-	name: "res5b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5b"
-	name: "res5b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "bn5c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "scale5c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "bn5c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "scale5c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2c"
-	name: "res5c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "bn5c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "scale5c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b"
-	bottom: "res5c_branch2c"
-	top: "res5c"
-	name: "res5c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "res5c"
-	name: "res5c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "pool5"
-	name: "pool5"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 7
-		stride: 1
-		pool: AVE
-	}
-}
-
-layer {
-	bottom: "pool5"
-	top: "fc1000"
-	name: "fc1000"
-	type: "InnerProduct"
-	inner_product_param {
-		num_output: 1000
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0
-    	        }
-	}
-}
-
-layer {
-	bottom: "fc1000"
-	bottom: "label"
-	top: "prob"
-	name: "prob"
-	type: "SoftmaxWithLoss"
-	include {
-	  phase: TRAIN
-	}
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/resnet_50/knm/solver_dummydata.prototxt b/models/intel_optimized_models/resnet_50/knm/solver_dummydata.prototxt
deleted file mode 100644
index 3d241ead2..000000000
--- a/models/intel_optimized_models/resnet_50/knm/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/resnet_50/knm/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/resnet_50/knm/resnet_50_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/knm/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/knm/train_val_dummydata.prototxt
deleted file mode 100644
index 00c35771f..000000000
--- a/models/intel_optimized_models/resnet_50/knm/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,3051 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64  }
-    data_filler {
-      type: "constant"
-    }
-  }
-}
-
-layer {
-	bottom: "data"
-	top: "conv1"
-	name: "conv1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 7
-		pad: 3
-		stride: 2
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "bn_conv1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "scale_conv1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "conv1_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "conv1"
-	top: "pool1"
-	name: "pool1"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 3
-		stride: 2
-		pool: MAX
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch1"
-	name: "res2a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "bn2a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "scale2a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "bn2a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "scale2a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "bn2a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "scale2a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2c"
-	name: "res2a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "bn2a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "scale2a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	bottom: "res2a_branch2c"
-	top: "res2a"
-	name: "res2a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2a"
-	name: "res2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "bn2b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "scale2b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "bn2b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "scale2b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2c"
-	name: "res2b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "bn2b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "scale2b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a"
-	bottom: "res2b_branch2c"
-	top: "res2b"
-	name: "res2b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2b"
-	name: "res2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "bn2c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "scale2c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "bn2c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "scale2c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2c"
-	name: "res2c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "bn2c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "scale2c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b"
-	bottom: "res2c_branch2c"
-	top: "res2c"
-	name: "res2c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res2c"
-	name: "res2c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch1"
-	name: "res3a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "bn3a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "scale3a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "bn3a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "scale3a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "bn3a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "scale3a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2c"
-	name: "res3a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "bn3a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "scale3a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	bottom: "res3a_branch2c"
-	top: "res3a"
-	name: "res3a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3a"
-	name: "res3a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "bn3b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "scale3b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "bn3b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "scale3b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2c"
-	name: "res3b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "bn3b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "scale3b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a"
-	bottom: "res3b_branch2c"
-	top: "res3b"
-	name: "res3b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3b"
-	name: "res3b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "bn3c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "scale3c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "bn3c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "scale3c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2c"
-	name: "res3c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "bn3c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "scale3c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b"
-	bottom: "res3c_branch2c"
-	top: "res3c"
-	name: "res3c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3c"
-	name: "res3c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "bn3d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "scale3d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "bn3d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "scale3d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2c"
-	name: "res3d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "bn3d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "scale3d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c"
-	bottom: "res3d_branch2c"
-	top: "res3d"
-	name: "res3d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res3d"
-	name: "res3d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch1"
-	name: "res4a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "bn4a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "scale4a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "bn4a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "scale4a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "bn4a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "scale4a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2c"
-	name: "res4a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "bn4a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "scale4a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	bottom: "res4a_branch2c"
-	top: "res4a"
-	name: "res4a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4a"
-	name: "res4a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "bn4b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "scale4b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "bn4b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "scale4b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2c"
-	name: "res4b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "bn4b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "scale4b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a"
-	bottom: "res4b_branch2c"
-	top: "res4b"
-	name: "res4b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4b"
-	name: "res4b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "bn4c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "scale4c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "bn4c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "scale4c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2c"
-	name: "res4c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "bn4c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "scale4c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b"
-	bottom: "res4c_branch2c"
-	top: "res4c"
-	name: "res4c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4c"
-	name: "res4c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "bn4d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "scale4d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "bn4d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "scale4d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2c"
-	name: "res4d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "bn4d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "scale4d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c"
-	bottom: "res4d_branch2c"
-	top: "res4d"
-	name: "res4d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4d"
-	name: "res4d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "bn4e_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "scale4e_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "bn4e_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "scale4e_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2c"
-	name: "res4e_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "bn4e_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "scale4e_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d"
-	bottom: "res4e_branch2c"
-	top: "res4e"
-	name: "res4e"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4e"
-	name: "res4e_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "bn4f_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "scale4f_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "bn4f_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "scale4f_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2c"
-	name: "res4f_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "bn4f_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "scale4f_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e"
-	bottom: "res4f_branch2c"
-	top: "res4f"
-	name: "res4f"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res4f"
-	name: "res4f_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch1"
-	name: "res5a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "bn5a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "scale5a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "bn5a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "scale5a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "bn5a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "scale5a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2c"
-	name: "res5a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "bn5a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "scale5a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	bottom: "res5a_branch2c"
-	top: "res5a"
-	name: "res5a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5a"
-	name: "res5a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "bn5b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "scale5b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "bn5b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "scale5b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2c"
-	name: "res5b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "bn5b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "scale5b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a"
-	bottom: "res5b_branch2c"
-	top: "res5b"
-	name: "res5b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5b"
-	name: "res5b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "bn5c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "scale5c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "bn5c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "scale5c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2c"
-	name: "res5c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "bn5c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "scale5c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b"
-	bottom: "res5c_branch2c"
-	top: "res5c"
-	name: "res5c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "res5c"
-	name: "res5c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "pool5"
-	name: "pool5"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 7
-		stride: 1
-		pool: AVE
-	}
-}
-
-layer {
-	bottom: "pool5"
-	top: "fc1000"
-	name: "fc1000"
-	type: "InnerProduct"
-	inner_product_param {
-		num_output: 1000
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0
-    	        }
-	}
-}
-
-layer {
-	bottom: "fc1000"
-	bottom: "label"
-	top: "prob"
-	name: "prob"
-	type: "SoftmaxWithLoss"
-	include {
-	  phase: TRAIN
-	}
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/resnet_50/skx/solver_dummydata.prototxt b/models/intel_optimized_models/resnet_50/skx/solver_dummydata.prototxt
deleted file mode 100644
index e3e78b51d..000000000
--- a/models/intel_optimized_models/resnet_50/skx/solver_dummydata.prototxt
+++ /dev/null
@@ -1,25 +0,0 @@
-#This is Intel(R) optimized (in terms of time to train) version of solver for model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
-#Original solver.prototxt can be found in /models/bvlc_alexnet/ directory of this repository.
-#Differences:
-#- lr_policy is set to poly instead of step
-#- base_lr is decreased to 0.007
-#- max_iter is decreased to 250000
-#- power is set to 0.6
-#
-#Top-5 and Top-1 results achieved with this version of solver:
-#Top-5: 80.4%
-#Top-1: 57.4%
-#Training was performed using server equipped with Intel(R) Xeon Phi(TM) CPU 7250 processor. 
-net: "models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt"
-test_iter: 1000
-test_interval: 10000
-base_lr: 0.007
-lr_policy: "poly"
-power: 0.6
-display: 1
-max_iter: 5000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 50000
-snapshot_prefix: "models/intel_optimized_models/resnet_50/skx/resnet_50_train"
-solver_mode: CPU
diff --git a/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt
deleted file mode 100644
index 00c35771f..000000000
--- a/models/intel_optimized_models/resnet_50/skx/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,3051 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64 dim: 3 dim: 224 dim: 224 }
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    shape: { dim: 64  }
-    data_filler {
-      type: "constant"
-    }
-  }
-}
-
-layer {
-	bottom: "data"
-	top: "conv1"
-	name: "conv1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 7
-		pad: 3
-		stride: 2
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "bn_conv1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "scale_conv1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "conv1_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "conv1"
-	top: "pool1"
-	name: "pool1"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 3
-		stride: 2
-		pool: MAX
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch1"
-	name: "res2a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "bn2a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "scale2a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "bn2a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "scale2a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "bn2a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "scale2a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2c"
-	name: "res2a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "bn2a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "scale2a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	bottom: "res2a_branch2c"
-	top: "res2a"
-	name: "res2a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2a"
-	name: "res2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "bn2b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "scale2b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "bn2b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "scale2b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2c"
-	name: "res2b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "bn2b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "scale2b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a"
-	bottom: "res2b_branch2c"
-	top: "res2b"
-	name: "res2b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2b"
-	name: "res2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "bn2c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "scale2c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "bn2c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "scale2c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2c"
-	name: "res2c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "bn2c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "scale2c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b"
-	bottom: "res2c_branch2c"
-	top: "res2c"
-	name: "res2c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res2c"
-	name: "res2c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch1"
-	name: "res3a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "bn3a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "scale3a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "bn3a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "scale3a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "bn3a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "scale3a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2c"
-	name: "res3a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "bn3a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "scale3a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	bottom: "res3a_branch2c"
-	top: "res3a"
-	name: "res3a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3a"
-	name: "res3a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "bn3b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "scale3b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "bn3b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "scale3b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2c"
-	name: "res3b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "bn3b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "scale3b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a"
-	bottom: "res3b_branch2c"
-	top: "res3b"
-	name: "res3b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3b"
-	name: "res3b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "bn3c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "scale3c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "bn3c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "scale3c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2c"
-	name: "res3c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "bn3c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "scale3c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b"
-	bottom: "res3c_branch2c"
-	top: "res3c"
-	name: "res3c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3c"
-	name: "res3c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "bn3d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "scale3d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "bn3d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "scale3d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2c"
-	name: "res3d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "bn3d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "scale3d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c"
-	bottom: "res3d_branch2c"
-	top: "res3d"
-	name: "res3d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res3d"
-	name: "res3d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch1"
-	name: "res4a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "bn4a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "scale4a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "bn4a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "scale4a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "bn4a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "scale4a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2c"
-	name: "res4a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "bn4a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "scale4a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	bottom: "res4a_branch2c"
-	top: "res4a"
-	name: "res4a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4a"
-	name: "res4a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "bn4b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "scale4b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "bn4b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "scale4b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2c"
-	name: "res4b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "bn4b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "scale4b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a"
-	bottom: "res4b_branch2c"
-	top: "res4b"
-	name: "res4b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4b"
-	name: "res4b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "bn4c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "scale4c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "bn4c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "scale4c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2c"
-	name: "res4c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "bn4c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "scale4c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b"
-	bottom: "res4c_branch2c"
-	top: "res4c"
-	name: "res4c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4c"
-	name: "res4c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "bn4d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "scale4d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "bn4d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "scale4d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2c"
-	name: "res4d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "bn4d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "scale4d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c"
-	bottom: "res4d_branch2c"
-	top: "res4d"
-	name: "res4d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4d"
-	name: "res4d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "bn4e_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "scale4e_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "bn4e_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "scale4e_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2c"
-	name: "res4e_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "bn4e_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "scale4e_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d"
-	bottom: "res4e_branch2c"
-	top: "res4e"
-	name: "res4e"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4e"
-	name: "res4e_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "bn4f_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "scale4f_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "bn4f_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "scale4f_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2c"
-	name: "res4f_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "bn4f_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "scale4f_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e"
-	bottom: "res4f_branch2c"
-	top: "res4f"
-	name: "res4f"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res4f"
-	name: "res4f_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch1"
-	name: "res5a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "bn5a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "scale5a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "bn5a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "scale5a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "bn5a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "scale5a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2c"
-	name: "res5a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "bn5a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "scale5a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	bottom: "res5a_branch2c"
-	top: "res5a"
-	name: "res5a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5a"
-	name: "res5a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "bn5b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "scale5b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "bn5b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "scale5b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2c"
-	name: "res5b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "bn5b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "scale5b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a"
-	bottom: "res5b_branch2c"
-	top: "res5b"
-	name: "res5b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5b"
-	name: "res5b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "bn5c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "scale5c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "bn5c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "scale5c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2c"
-	name: "res5c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "bn5c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "scale5c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b"
-	bottom: "res5c_branch2c"
-	top: "res5c"
-	name: "res5c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "res5c"
-	name: "res5c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "pool5"
-	name: "pool5"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 7
-		stride: 1
-		pool: AVE
-	}
-}
-
-layer {
-	bottom: "pool5"
-	top: "fc1000"
-	name: "fc1000"
-	type: "InnerProduct"
-	inner_product_param {
-		num_output: 1000
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0
-    	        }
-	}
-}
-
-layer {
-	bottom: "fc1000"
-	bottom: "label"
-	top: "prob"
-	name: "prob"
-	type: "SoftmaxWithLoss"
-	include {
-	  phase: TRAIN
-	}
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/resnet_50/train_val.prototxt b/models/intel_optimized_models/resnet_50/train_val.prototxt
deleted file mode 100644
index 75bff6269..000000000
--- a/models/intel_optimized_models/resnet_50/train_val.prototxt
+++ /dev/null
@@ -1,3322 +0,0 @@
-name: "ResNet-50"
-bn_stats_batch_size: 32
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  transform_param {
-    mirror: true
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_aspect_ratio_param {
-      min_area_ratio: 0.08
-      max_area_ratio: 1
-      aspect_ratio_change: 0.75
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 128
-    backend: LMDB
-    prefetch: 2
-    shuffle: true
-  }
-}
-layer {
-  name: "data"
-  type: "Data"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  transform_param {
-    mirror: false
-    crop_size: 224
-    scale: 0.0078125
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    random_resize_param {
-      min_size: 256
-      max_size: 256
-      resize_param {
-        interp_mode: CUBIC
-      }
-    }
-  }
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    batch_size: 50
-    backend: LMDB
-  }
-}
-
-layer {
-  bottom: "data"
-  top: "conv1"
-  name: "conv1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 7
-    pad: 3
-    stride: 2
-    weight_filler {
-      type: "msra"
-      variance_norm: FAN_OUT
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "bn_conv1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "scale_conv1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "conv1"
-  name: "conv1_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "conv1"
-  top: "pool1"
-  name: "pool1"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 3
-    stride: 2
-    pool: MAX
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch1"
-  name: "res2a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "bn2a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  name: "scale2a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "pool1"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "bn2a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "scale2a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  name: "res2a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "bn2a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "scale2a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  name: "res2a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2c"
-  name: "res2a_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "bn2a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  name: "scale2a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a_branch1"
-  bottom: "res2a_branch2c"
-  top: "res2a"
-  name: "res2a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2a"
-  name: "res2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "bn2b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "scale2b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  name: "res2b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "bn2b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "scale2b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  name: "res2b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2c"
-  name: "res2b_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "bn2b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  name: "scale2b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2a"
-  bottom: "res2b_branch2c"
-  top: "res2b"
-  name: "res2b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2b"
-  name: "res2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2b"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 64
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "bn2c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "scale2c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  name: "res2c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 64
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "bn2c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "scale2c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  name: "res2c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2c"
-  name: "res2c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "bn2c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  name: "scale2c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2b"
-  bottom: "res2c_branch2c"
-  top: "res2c"
-  name: "res2c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res2c"
-  name: "res2c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch1"
-  name: "res3a_branch1"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "bn3a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  name: "scale3a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res2c"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "bn3a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "scale3a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  name: "res3a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "bn3a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "scale3a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  name: "res3a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2c"
-  name: "res3a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "bn3a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  name: "scale3a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a_branch1"
-  bottom: "res3a_branch2c"
-  top: "res3a"
-  name: "res3a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3a"
-  name: "res3a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "bn3b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "scale3b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  name: "res3b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "bn3b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "scale3b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  name: "res3b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2c"
-  name: "res3b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "bn3b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  name: "scale3b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3a"
-  bottom: "res3b_branch2c"
-  top: "res3b"
-  name: "res3b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3b"
-  name: "res3b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3b"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "bn3c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "scale3c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  name: "res3c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "bn3c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "scale3c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  name: "res3c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2c"
-  name: "res3c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "bn3c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  name: "scale3c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3b"
-  bottom: "res3c_branch2c"
-  top: "res3c"
-  name: "res3c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3c"
-  name: "res3c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3c"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "bn3d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "scale3d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  name: "res3d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 128
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "bn3d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "scale3d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  name: "res3d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2c"
-  name: "res3d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "bn3d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  name: "scale3d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3c"
-  bottom: "res3d_branch2c"
-  top: "res3d"
-  name: "res3d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res3d"
-  name: "res3d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch1"
-  name: "res4a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "bn4a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  name: "scale4a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res3d"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "bn4a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "scale4a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  name: "res4a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "bn4a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "scale4a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  name: "res4a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2c"
-  name: "res4a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "bn4a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  name: "scale4a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a_branch1"
-  bottom: "res4a_branch2c"
-  top: "res4a"
-  name: "res4a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4a"
-  name: "res4a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "bn4b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "scale4b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  name: "res4b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "bn4b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "scale4b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  name: "res4b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2c"
-  name: "res4b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "bn4b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  name: "scale4b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4a"
-  bottom: "res4b_branch2c"
-  top: "res4b"
-  name: "res4b"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4b"
-  name: "res4b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4b"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "bn4c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "scale4c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  name: "res4c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "bn4c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "scale4c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  name: "res4c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2c"
-  name: "res4c_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "bn4c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  name: "scale4c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4b"
-  bottom: "res4c_branch2c"
-  top: "res4c"
-  name: "res4c"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4c"
-  name: "res4c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4c"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "bn4d_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "scale4d_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  name: "res4d_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "bn4d_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "scale4d_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  name: "res4d_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2c"
-  name: "res4d_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "bn4d_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  name: "scale4d_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4c"
-  bottom: "res4d_branch2c"
-  top: "res4d"
-  name: "res4d"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4d"
-  name: "res4d_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4d"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "bn4e_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "scale4e_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  name: "res4e_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "bn4e_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "scale4e_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  name: "res4e_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2c"
-  name: "res4e_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "bn4e_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  name: "scale4e_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4d"
-  bottom: "res4e_branch2c"
-  top: "res4e"
-  name: "res4e"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4e"
-  name: "res4e_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4e"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "bn4f_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "scale4f_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  name: "res4f_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 256
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "bn4f_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "scale4f_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  name: "res4f_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2c"
-  name: "res4f_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 1024
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "bn4f_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  name: "scale4f_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4e"
-  bottom: "res4f_branch2c"
-  top: "res4f"
-  name: "res4f"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res4f"
-  name: "res4f_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch1"
-  name: "res5a_branch1"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "bn5a_branch1"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  name: "scale5a_branch1"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res4f"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "bn5a_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "scale5a_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  name: "res5a_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 2
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "bn5a_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "scale5a_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  name: "res5a_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2c"
-  name: "res5a_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "bn5a_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  name: "scale5a_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a_branch1"
-  bottom: "res5a_branch2c"
-  top: "res5a"
-  name: "res5a"
-  type: "Eltwise"
-  eltwise_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5a"
-  name: "res5a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "bn5b_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "scale5b_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  name: "res5b_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "bn5b_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "scale5b_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  name: "res5b_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2c"
-  name: "res5b_branch2c"
-  type: "Convolution"
-  convolution_param {
-
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "bn5b_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  name: "scale5b_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5a"
-  bottom: "res5b_branch2c"
-  top: "res5b"
-  name: "res5b"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5b"
-  name: "res5b_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5b"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "bn5c_branch2a"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "scale5c_branch2a"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  name: "res5c_branch2a_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b"
-  type: "Convolution"
-  convolution_param {
-    num_output: 512
-    kernel_size: 3
-    pad: 1
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "bn5c_branch2b"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-    filler { value: 1 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "scale5c_branch2b"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  name: "res5c_branch2b_relu"
-  type: "ReLU"
-  relu_param {
-  }
-}
-
-layer {
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2c"
-  name: "res5c_branch2c"
-  type: "Convolution"
-  convolution_param {
-    num_output: 2048
-    kernel_size: 1
-    pad: 0
-    stride: 1
-    bias_term: false
-    weight_filler {
-      type: "msra"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "bn5c_branch2c"
-  type: "BatchNorm"
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  param { lr_mult: 0 }
-  batch_norm_param {
-    moving_average_fraction: 0.9
-  filler { value: 0 }
-  }
-}
-
-layer {
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  name: "scale5c_branch2c"
-  type: "Scale"
-  param { decay_mult: 0 }
-  param { decay_mult: 0 }
-  scale_param {
-    bias_term: true
-  }
-}
-
-layer {
-  bottom: "res5b"
-  bottom: "res5c_branch2c"
-  top: "res5c"
-  name: "res5c"
-  type: "Eltwise"
-  eltwise_param {
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "res5c"
-  name: "res5c_relu"
-  type: "ReLU"
-  relu_param {
-
-  }
-}
-
-layer {
-  bottom: "res5c"
-  top: "pool5"
-  name: "pool5"
-  type: "Pooling"
-  pooling_param {
-    kernel_size: 7
-    stride: 1
-    pool: AVE
-  }
-}
-
-layer {
-  bottom: "pool5"
-  top: "fc1000"
-  name: "fc1000"
-  type: "InnerProduct"
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-
-layer {
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss"
-  name: "prob"
-  type: "SoftmaxWithLoss"
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/resnet_50/train_val_dummydata.prototxt b/models/intel_optimized_models/resnet_50/train_val_dummydata.prototxt
deleted file mode 100644
index 15301d2b5..000000000
--- a/models/intel_optimized_models/resnet_50/train_val_dummydata.prototxt
+++ /dev/null
@@ -1,3055 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TRAIN
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 32 dim: 1 dim: 1 dim: 1  }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  top: "label"
-  include {
-    phase: TEST
-  }
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape: { dim: 32 dim: 3 dim: 224 dim: 224 }
-    shape: { dim: 32 dim: 1 dim: 1   dim: 1   }
-  }
-}
-layer {
-	bottom: "data"
-	top: "conv1"
-	name: "conv1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 7
-		pad: 3
-		stride: 2
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "bn_conv1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "scale_conv1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "conv1"
-	top: "conv1"
-	name: "conv1_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "conv1"
-	top: "pool1"
-	name: "pool1"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 3
-		stride: 2
-		pool: MAX
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch1"
-	name: "res2a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "bn2a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	top: "res2a_branch1"
-	name: "scale2a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "pool1"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "bn2a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "scale2a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2a"
-	name: "res2a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2a"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "bn2a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "scale2a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2b"
-	name: "res2a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a_branch2b"
-	top: "res2a_branch2c"
-	name: "res2a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "bn2a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2a_branch2c"
-	top: "res2a_branch2c"
-	name: "scale2a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a_branch1"
-	bottom: "res2a_branch2c"
-	top: "res2a"
-	name: "res2a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2a"
-	name: "res2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "bn2b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "scale2b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2a"
-	name: "res2b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2a"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "bn2b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "scale2b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2b"
-	name: "res2b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b_branch2b"
-	top: "res2b_branch2c"
-	name: "res2b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "bn2b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2b_branch2c"
-	top: "res2b_branch2c"
-	name: "scale2b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2a"
-	bottom: "res2b_branch2c"
-	top: "res2b"
-	name: "res2b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2b"
-	name: "res2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2b"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "bn2c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "scale2c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2a"
-	name: "res2c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2a"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 64
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "bn2c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "scale2c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2b"
-	name: "res2c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c_branch2b"
-	top: "res2c_branch2c"
-	name: "res2c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "bn2c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res2c_branch2c"
-	top: "res2c_branch2c"
-	name: "scale2c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2b"
-	bottom: "res2c_branch2c"
-	top: "res2c"
-	name: "res2c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res2c"
-	name: "res2c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch1"
-	name: "res3a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "bn3a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	top: "res3a_branch1"
-	name: "scale3a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res2c"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "bn3a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "scale3a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2a"
-	name: "res3a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2a"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "bn3a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "scale3a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2b"
-	name: "res3a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a_branch2b"
-	top: "res3a_branch2c"
-	name: "res3a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "bn3a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3a_branch2c"
-	top: "res3a_branch2c"
-	name: "scale3a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a_branch1"
-	bottom: "res3a_branch2c"
-	top: "res3a"
-	name: "res3a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3a"
-	name: "res3a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "bn3b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "scale3b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2a"
-	name: "res3b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2a"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "bn3b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "scale3b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2b"
-	name: "res3b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b_branch2b"
-	top: "res3b_branch2c"
-	name: "res3b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "bn3b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3b_branch2c"
-	top: "res3b_branch2c"
-	name: "scale3b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3a"
-	bottom: "res3b_branch2c"
-	top: "res3b"
-	name: "res3b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3b"
-	name: "res3b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3b"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "bn3c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "scale3c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2a"
-	name: "res3c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2a"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "bn3c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "scale3c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2b"
-	name: "res3c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c_branch2b"
-	top: "res3c_branch2c"
-	name: "res3c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "bn3c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3c_branch2c"
-	top: "res3c_branch2c"
-	name: "scale3c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3b"
-	bottom: "res3c_branch2c"
-	top: "res3c"
-	name: "res3c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3c"
-	name: "res3c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3c"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "bn3d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "scale3d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2a"
-	name: "res3d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2a"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 128
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "bn3d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "scale3d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2b"
-	name: "res3d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d_branch2b"
-	top: "res3d_branch2c"
-	name: "res3d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "bn3d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res3d_branch2c"
-	top: "res3d_branch2c"
-	name: "scale3d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3c"
-	bottom: "res3d_branch2c"
-	top: "res3d"
-	name: "res3d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res3d"
-	name: "res3d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch1"
-	name: "res4a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "bn4a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	top: "res4a_branch1"
-	name: "scale4a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res3d"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "bn4a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "scale4a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2a"
-	name: "res4a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2a"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "bn4a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "scale4a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2b"
-	name: "res4a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a_branch2b"
-	top: "res4a_branch2c"
-	name: "res4a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "bn4a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4a_branch2c"
-	top: "res4a_branch2c"
-	name: "scale4a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a_branch1"
-	bottom: "res4a_branch2c"
-	top: "res4a"
-	name: "res4a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4a"
-	name: "res4a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "bn4b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "scale4b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2a"
-	name: "res4b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2a"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "bn4b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "scale4b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2b"
-	name: "res4b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b_branch2b"
-	top: "res4b_branch2c"
-	name: "res4b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "bn4b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4b_branch2c"
-	top: "res4b_branch2c"
-	name: "scale4b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4a"
-	bottom: "res4b_branch2c"
-	top: "res4b"
-	name: "res4b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4b"
-	name: "res4b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4b"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "bn4c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "scale4c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2a"
-	name: "res4c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2a"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "bn4c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "scale4c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2b"
-	name: "res4c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c_branch2b"
-	top: "res4c_branch2c"
-	name: "res4c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "bn4c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4c_branch2c"
-	top: "res4c_branch2c"
-	name: "scale4c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4b"
-	bottom: "res4c_branch2c"
-	top: "res4c"
-	name: "res4c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4c"
-	name: "res4c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4c"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "bn4d_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "scale4d_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2a"
-	name: "res4d_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2a"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "bn4d_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "scale4d_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2b"
-	name: "res4d_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d_branch2b"
-	top: "res4d_branch2c"
-	name: "res4d_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "bn4d_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4d_branch2c"
-	top: "res4d_branch2c"
-	name: "scale4d_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4c"
-	bottom: "res4d_branch2c"
-	top: "res4d"
-	name: "res4d"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4d"
-	name: "res4d_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4d"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "bn4e_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "scale4e_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2a"
-	name: "res4e_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2a"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "bn4e_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "scale4e_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2b"
-	name: "res4e_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e_branch2b"
-	top: "res4e_branch2c"
-	name: "res4e_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "bn4e_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4e_branch2c"
-	top: "res4e_branch2c"
-	name: "scale4e_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4d"
-	bottom: "res4e_branch2c"
-	top: "res4e"
-	name: "res4e"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4e"
-	name: "res4e_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4e"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "bn4f_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "scale4f_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2a"
-	name: "res4f_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2a"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 256
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "bn4f_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "scale4f_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2b"
-	name: "res4f_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f_branch2b"
-	top: "res4f_branch2c"
-	name: "res4f_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 1024
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "bn4f_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res4f_branch2c"
-	top: "res4f_branch2c"
-	name: "scale4f_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4e"
-	bottom: "res4f_branch2c"
-	top: "res4f"
-	name: "res4f"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res4f"
-	name: "res4f_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch1"
-	name: "res5a_branch1"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "bn5a_branch1"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	top: "res5a_branch1"
-	name: "scale5a_branch1"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res4f"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 2
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "bn5a_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "scale5a_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2a"
-	name: "res5a_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2a"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "bn5a_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "scale5a_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2b"
-	name: "res5a_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a_branch2b"
-	top: "res5a_branch2c"
-	name: "res5a_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "bn5a_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5a_branch2c"
-	top: "res5a_branch2c"
-	name: "scale5a_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a_branch1"
-	bottom: "res5a_branch2c"
-	top: "res5a"
-	name: "res5a"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5a"
-	name: "res5a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "bn5b_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "scale5b_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2a"
-	name: "res5b_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2a"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "bn5b_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "scale5b_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2b"
-	name: "res5b_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b_branch2b"
-	top: "res5b_branch2c"
-	name: "res5b_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "bn5b_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5b_branch2c"
-	top: "res5b_branch2c"
-	name: "scale5b_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5a"
-	bottom: "res5b_branch2c"
-	top: "res5b"
-	name: "res5b"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5b"
-	name: "res5b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5b"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "bn5c_branch2a"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "scale5c_branch2a"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2a"
-	name: "res5c_branch2a_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2a"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 512
-		kernel_size: 3
-		pad: 1
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "bn5c_branch2b"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "scale5c_branch2b"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2b"
-	name: "res5c_branch2b_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c_branch2b"
-	top: "res5c_branch2c"
-	name: "res5c_branch2c"
-	type: "Convolution"
-	convolution_param {
-    
-		num_output: 2048
-		kernel_size: 1
-		pad: 0
-		stride: 1
-		bias_term: false
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0.2
-    	       }
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "bn5c_branch2c"
-	type: "BatchNorm"
-	batch_norm_param {
-    
-		
-	}
-}
-
-layer {
-	bottom: "res5c_branch2c"
-	top: "res5c_branch2c"
-	name: "scale5c_branch2c"
-	type: "Scale"
-	scale_param {
-		bias_term: true
-	}
-}
-
-layer {
-	bottom: "res5b"
-	bottom: "res5c_branch2c"
-	top: "res5c"
-	name: "res5c"
-	type: "Eltwise"
-  eltwise_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "res5c"
-	name: "res5c_relu"
-	type: "ReLU"
-  relu_param {
-    
-  }
-}
-
-layer {
-	bottom: "res5c"
-	top: "pool5"
-	name: "pool5"
-	type: "Pooling"
-	pooling_param {
-    
-		kernel_size: 7
-		stride: 1
-		pool: AVE
-	}
-}
-
-layer {
-	bottom: "pool5"
-	top: "fc1000"
-	name: "fc1000"
-	type: "InnerProduct"
-	inner_product_param {
-		num_output: 1000
-		weight_filler {
-      		  type: "xavier"
-    		}
-    		bias_filler {
-      		  type: "constant"
-      		  value: 0
-    	        }
-	}
-}
-
-layer {
-	bottom: "fc1000"
-	bottom: "label"
-	top: "prob"
-	name: "prob"
-	type: "SoftmaxWithLoss"
-	include {
-	  phase: TRAIN
-	}
-}
-layer {
-  name: "loss3/top-1"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-1"
-  include {
-    phase: TEST
-  }
-}
-layer {
-  name: "loss3/top-5"
-  type: "Accuracy"
-  bottom: "fc1000"
-  bottom: "label"
-  top: "loss3/top-5"
-  include {
-    phase: TEST
-  }
-  accuracy_param {
-    top_k: 5
-  }
-}
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt
new file mode 100644
index 000000000..f7c725d42
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt
@@ -0,0 +1,7098 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "rois"
+input_shape {
+  dim: 1 # to be changed on-the-fly to num ROIs
+  dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 3969 #81*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 81
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 81
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt
new file mode 100644
index 000000000..aefca1ac0
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt
@@ -0,0 +1,6352 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+#-----------------------layer +-------------------------
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#-----------------------output------------------------
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 24 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  top: 'scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver360k480k.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver360k480k.pt
new file mode 100644
index 000000000..664f1625c
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver360k480k.pt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 360000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
new file mode 100644
index 000000000..2a6d1e6d2
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
@@ -0,0 +1,7178 @@
+name: "ResNet-101"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 3969 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 81
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver360k480k.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver360k480k.pt
new file mode 100644
index 000000000..c4fc9e495
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver360k480k.pt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 360000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
new file mode 100644
index 000000000..00f2aae50
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver360k480k.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver360k480k.pt
new file mode 100644
index 000000000..6ec710a50
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver360k480k.pt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 360000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
new file mode 100644
index 000000000..2a6d1e6d2
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
@@ -0,0 +1,7178 @@
+name: "ResNet-101"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 3969 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 81
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver360k480k.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver360k480k.pt
new file mode 100644
index 000000000..b36b43941
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver360k480k.pt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 360000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
new file mode 100644
index 000000000..8ae6e340c
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver360k480k.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver360k480k.pt
new file mode 100644
index 000000000..0af95b51e
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver360k480k.pt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 360000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
new file mode 100644
index 000000000..8ae6e340c
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver.prototxt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver.prototxt
new file mode 100644
index 000000000..488ce6ef5
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver.prototxt
@@ -0,0 +1,15 @@
+train_net: "models/coco/ResNet-101/rfcn_end2end/train_agnostic.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 640000
+display: 100
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn"
+# debug_info: true
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver_ohem.prototxt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver_ohem.prototxt
new file mode 100644
index 000000000..392fdd5c9
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/solver_ohem.prototxt
@@ -0,0 +1,16 @@
+train_net: "models/coco/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt"
+base_lr: 0.0005
+lr_policy: "step"
+gamma: 0.1
+stepsize: 1280000
+display: 100
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+average_loss: 100
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+# debug_info: true
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/test_agnostic.prototxt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/test_agnostic.prototxt
new file mode 100644
index 000000000..10418c86d
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/test_agnostic.prototxt
@@ -0,0 +1,7186 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 24 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 3969 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 81
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 81
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic.prototxt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic.prototxt
new file mode 100644
index 000000000..1d26dda92
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic.prototxt
@@ -0,0 +1,7273 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 24 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "loss_cls"
+   loss_weight: 1
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: 'bbox_inside_weights'
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+    name: "silence"
+    type: "Silence"
+    bottom: "bbox_outside_weights"
+}
+
diff --git a/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt
new file mode 100644
index 000000000..bd2d38a5b
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/coco/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt
@@ -0,0 +1,7343 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 81"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 24   # 2(bg/fg) * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 48   # 4 * 12(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 24 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16 \n'scales': !!python/tuple [4, 8, 16, 32]"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 3969 #81*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 81
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt
new file mode 100644
index 000000000..03da0126f
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rfcn_test.pt
@@ -0,0 +1,7098 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "rois"
+input_shape {
+  dim: 1 # to be changed on-the-fly to num ROIs
+  dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt
new file mode 100644
index 000000000..57d3e7095
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/rpn_test.pt
@@ -0,0 +1,6352 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+#-----------------------layer +-------------------------
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#-----------------------output------------------------
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  top: 'scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt
new file mode 100644
index 000000000..e3bd7cb28
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
new file mode 100644
index 000000000..48d1492dd
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
@@ -0,0 +1,7178 @@
+name: "ResNet-101"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt
new file mode 100644
index 000000000..51d772dcf
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
new file mode 100644
index 000000000..d839e6173
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt
new file mode 100644
index 000000000..4b6729648
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
new file mode 100644
index 000000000..48d1492dd
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
@@ -0,0 +1,7178 @@
+name: "ResNet-101"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt
new file mode 100644
index 000000000..8931c0081
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
new file mode 100644
index 000000000..e6273c9ae
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt
new file mode 100644
index 000000000..2a1e48c7b
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
new file mode 100644
index 000000000..e6273c9ae
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
@@ -0,0 +1,6364 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/test.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/test.prototxt
new file mode 100644
index 000000000..076dd0260
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/test.prototxt
@@ -0,0 +1,7186 @@
+name: "ResNet101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res5c"
+  top: "rpn/output"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 4116 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 84
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+    name: "cls_prob"
+    type: "Softmax"
+    bottom: "cls_score"
+    top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 84
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/train_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/train_ohem.prototxt
new file mode 100644
index 000000000..ae3ce444e
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/class-aware/train_ohem.prototxt
@@ -0,0 +1,7343 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res5c"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 4116 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 84
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver.prototxt
new file mode 100644
index 000000000..4ad5d37db
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver.prototxt
@@ -0,0 +1,18 @@
+train_net: "../../models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 1
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn"
+iter_size: 2
+# debug_info: true
+solver_mode: CPU
+# time_info: true
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver_ohem.prototxt
new file mode 100644
index 000000000..87b511867
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/solver_ohem.prototxt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet101_rfcn_ohem"
+iter_size: 2
+# debug_info: true
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic.prototxt
new file mode 100644
index 000000000..216353108
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic.prototxt
@@ -0,0 +1,7186 @@
+name: "ResNet-101"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt
new file mode 100644
index 000000000..ebc0ab814
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt
@@ -0,0 +1,7277 @@
+name: "ResNet-101"
+# time_info: true
+
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    engine: "CAFFE"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "loss_cls"
+   loss_weight: 1
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: 'bbox_inside_weights'
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+    name: "silence"
+    type: "Silence"
+    bottom: "bbox_outside_weights"
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt
new file mode 100644
index 000000000..a6847c447
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic_ohem.prototxt
@@ -0,0 +1,7344 @@
+name: "ResNet-101"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "conv1"
+    bottom: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2a"
+    bottom: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2a_branch2b"
+    bottom: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2a"
+    bottom: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2b_branch2b"
+    bottom: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2a"
+    bottom: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res2c_branch2b"
+    bottom: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2a"
+    bottom: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3a_branch2b"
+    bottom: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b1_branch2a"
+    name: "res3b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "bn3b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2a"
+    name: "scale3b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2a"
+    bottom: "res3b1_branch2a"
+    name: "res3b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2a"
+    top: "res3b1_branch2b"
+    name: "res3b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "bn3b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2b"
+    name: "scale3b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b1_branch2b"
+    bottom: "res3b1_branch2b"
+    name: "res3b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1_branch2b"
+    top: "res3b1_branch2c"
+    name: "res3b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "bn3b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1_branch2c"
+    top: "res3b1_branch2c"
+    name: "scale3b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b1_branch2c"
+    top: "res3b1"
+    name: "res3b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b1"
+    name: "res3b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b1"
+    top: "res3b2_branch2a"
+    name: "res3b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "bn3b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2a"
+    name: "scale3b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2a"
+    bottom: "res3b2_branch2a"
+    name: "res3b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2a"
+    top: "res3b2_branch2b"
+    name: "res3b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "bn3b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2b"
+    name: "scale3b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b2_branch2b"
+    bottom: "res3b2_branch2b"
+    name: "res3b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2_branch2b"
+    top: "res3b2_branch2c"
+    name: "res3b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "bn3b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2_branch2c"
+    top: "res3b2_branch2c"
+    name: "scale3b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b1"
+    bottom: "res3b2_branch2c"
+    top: "res3b2"
+    name: "res3b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b2"
+    name: "res3b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b2"
+    top: "res3b3_branch2a"
+    name: "res3b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "bn3b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2a"
+    name: "scale3b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2a"
+    bottom: "res3b3_branch2a"
+    name: "res3b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2a"
+    top: "res3b3_branch2b"
+    name: "res3b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "bn3b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2b"
+    name: "scale3b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res3b3_branch2b"
+    bottom: "res3b3_branch2b"
+    name: "res3b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3_branch2b"
+    top: "res3b3_branch2c"
+    name: "res3b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "bn3b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3_branch2c"
+    top: "res3b3_branch2c"
+    name: "scale3b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b2"
+    bottom: "res3b3_branch2c"
+    top: "res3b3"
+    name: "res3b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res3b3"
+    name: "res3b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b3"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2a"
+    bottom: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4a_branch2b"
+    bottom: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b1_branch2a"
+    name: "res4b1_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "bn4b1_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2a"
+    name: "scale4b1_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2a"
+    bottom: "res4b1_branch2a"
+    name: "res4b1_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2a"
+    top: "res4b1_branch2b"
+    name: "res4b1_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "bn4b1_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2b"
+    name: "scale4b1_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b1_branch2b"
+    bottom: "res4b1_branch2b"
+    name: "res4b1_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1_branch2b"
+    top: "res4b1_branch2c"
+    name: "res4b1_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "bn4b1_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1_branch2c"
+    top: "res4b1_branch2c"
+    name: "scale4b1_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b1_branch2c"
+    top: "res4b1"
+    name: "res4b1"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b1"
+    name: "res4b1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b1"
+    top: "res4b2_branch2a"
+    name: "res4b2_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "bn4b2_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2a"
+    name: "scale4b2_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2a"
+    bottom: "res4b2_branch2a"
+    name: "res4b2_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2a"
+    top: "res4b2_branch2b"
+    name: "res4b2_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "bn4b2_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2b"
+    name: "scale4b2_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b2_branch2b"
+    bottom: "res4b2_branch2b"
+    name: "res4b2_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2_branch2b"
+    top: "res4b2_branch2c"
+    name: "res4b2_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "bn4b2_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2_branch2c"
+    top: "res4b2_branch2c"
+    name: "scale4b2_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b1"
+    bottom: "res4b2_branch2c"
+    top: "res4b2"
+    name: "res4b2"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b2"
+    name: "res4b2_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b2"
+    top: "res4b3_branch2a"
+    name: "res4b3_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "bn4b3_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2a"
+    name: "scale4b3_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2a"
+    bottom: "res4b3_branch2a"
+    name: "res4b3_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2a"
+    top: "res4b3_branch2b"
+    name: "res4b3_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "bn4b3_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2b"
+    name: "scale4b3_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b3_branch2b"
+    bottom: "res4b3_branch2b"
+    name: "res4b3_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3_branch2b"
+    top: "res4b3_branch2c"
+    name: "res4b3_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "bn4b3_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3_branch2c"
+    top: "res4b3_branch2c"
+    name: "scale4b3_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b2"
+    bottom: "res4b3_branch2c"
+    top: "res4b3"
+    name: "res4b3"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b3"
+    name: "res4b3_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b3"
+    top: "res4b4_branch2a"
+    name: "res4b4_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "bn4b4_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2a"
+    name: "scale4b4_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2a"
+    bottom: "res4b4_branch2a"
+    name: "res4b4_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2a"
+    top: "res4b4_branch2b"
+    name: "res4b4_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "bn4b4_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2b"
+    name: "scale4b4_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b4_branch2b"
+    bottom: "res4b4_branch2b"
+    name: "res4b4_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4_branch2b"
+    top: "res4b4_branch2c"
+    name: "res4b4_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "bn4b4_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4_branch2c"
+    top: "res4b4_branch2c"
+    name: "scale4b4_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b3"
+    bottom: "res4b4_branch2c"
+    top: "res4b4"
+    name: "res4b4"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b4"
+    name: "res4b4_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b4"
+    top: "res4b5_branch2a"
+    name: "res4b5_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "bn4b5_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2a"
+    name: "scale4b5_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2a"
+    bottom: "res4b5_branch2a"
+    name: "res4b5_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2a"
+    top: "res4b5_branch2b"
+    name: "res4b5_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "bn4b5_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2b"
+    name: "scale4b5_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b5_branch2b"
+    bottom: "res4b5_branch2b"
+    name: "res4b5_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5_branch2b"
+    top: "res4b5_branch2c"
+    name: "res4b5_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "bn4b5_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5_branch2c"
+    top: "res4b5_branch2c"
+    name: "scale4b5_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b4"
+    bottom: "res4b5_branch2c"
+    top: "res4b5"
+    name: "res4b5"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b5"
+    name: "res4b5_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b5"
+    top: "res4b6_branch2a"
+    name: "res4b6_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "bn4b6_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2a"
+    name: "scale4b6_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2a"
+    bottom: "res4b6_branch2a"
+    name: "res4b6_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2a"
+    top: "res4b6_branch2b"
+    name: "res4b6_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "bn4b6_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2b"
+    name: "scale4b6_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b6_branch2b"
+    bottom: "res4b6_branch2b"
+    name: "res4b6_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6_branch2b"
+    top: "res4b6_branch2c"
+    name: "res4b6_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "bn4b6_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6_branch2c"
+    top: "res4b6_branch2c"
+    name: "scale4b6_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b5"
+    bottom: "res4b6_branch2c"
+    top: "res4b6"
+    name: "res4b6"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b6"
+    name: "res4b6_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b6"
+    top: "res4b7_branch2a"
+    name: "res4b7_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "bn4b7_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2a"
+    name: "scale4b7_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2a"
+    bottom: "res4b7_branch2a"
+    name: "res4b7_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2a"
+    top: "res4b7_branch2b"
+    name: "res4b7_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "bn4b7_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2b"
+    name: "scale4b7_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b7_branch2b"
+    bottom: "res4b7_branch2b"
+    name: "res4b7_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7_branch2b"
+    top: "res4b7_branch2c"
+    name: "res4b7_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "bn4b7_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7_branch2c"
+    top: "res4b7_branch2c"
+    name: "scale4b7_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b6"
+    bottom: "res4b7_branch2c"
+    top: "res4b7"
+    name: "res4b7"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b7"
+    name: "res4b7_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b7"
+    top: "res4b8_branch2a"
+    name: "res4b8_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "bn4b8_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2a"
+    name: "scale4b8_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2a"
+    bottom: "res4b8_branch2a"
+    name: "res4b8_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2a"
+    top: "res4b8_branch2b"
+    name: "res4b8_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "bn4b8_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2b"
+    name: "scale4b8_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b8_branch2b"
+    bottom: "res4b8_branch2b"
+    name: "res4b8_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8_branch2b"
+    top: "res4b8_branch2c"
+    name: "res4b8_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "bn4b8_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8_branch2c"
+    top: "res4b8_branch2c"
+    name: "scale4b8_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b7"
+    bottom: "res4b8_branch2c"
+    top: "res4b8"
+    name: "res4b8"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b8"
+    name: "res4b8_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b8"
+    top: "res4b9_branch2a"
+    name: "res4b9_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "bn4b9_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2a"
+    name: "scale4b9_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2a"
+    bottom: "res4b9_branch2a"
+    name: "res4b9_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2a"
+    top: "res4b9_branch2b"
+    name: "res4b9_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "bn4b9_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2b"
+    name: "scale4b9_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b9_branch2b"
+    bottom: "res4b9_branch2b"
+    name: "res4b9_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9_branch2b"
+    top: "res4b9_branch2c"
+    name: "res4b9_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "bn4b9_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9_branch2c"
+    top: "res4b9_branch2c"
+    name: "scale4b9_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b8"
+    bottom: "res4b9_branch2c"
+    top: "res4b9"
+    name: "res4b9"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b9"
+    name: "res4b9_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b9"
+    top: "res4b10_branch2a"
+    name: "res4b10_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "bn4b10_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2a"
+    name: "scale4b10_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2a"
+    bottom: "res4b10_branch2a"
+    name: "res4b10_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2a"
+    top: "res4b10_branch2b"
+    name: "res4b10_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "bn4b10_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2b"
+    name: "scale4b10_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b10_branch2b"
+    bottom: "res4b10_branch2b"
+    name: "res4b10_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10_branch2b"
+    top: "res4b10_branch2c"
+    name: "res4b10_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "bn4b10_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10_branch2c"
+    top: "res4b10_branch2c"
+    name: "scale4b10_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b9"
+    bottom: "res4b10_branch2c"
+    top: "res4b10"
+    name: "res4b10"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b10"
+    name: "res4b10_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b10"
+    top: "res4b11_branch2a"
+    name: "res4b11_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "bn4b11_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2a"
+    name: "scale4b11_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2a"
+    bottom: "res4b11_branch2a"
+    name: "res4b11_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2a"
+    top: "res4b11_branch2b"
+    name: "res4b11_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "bn4b11_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2b"
+    name: "scale4b11_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b11_branch2b"
+    bottom: "res4b11_branch2b"
+    name: "res4b11_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11_branch2b"
+    top: "res4b11_branch2c"
+    name: "res4b11_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "bn4b11_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11_branch2c"
+    top: "res4b11_branch2c"
+    name: "scale4b11_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b10"
+    bottom: "res4b11_branch2c"
+    top: "res4b11"
+    name: "res4b11"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b11"
+    name: "res4b11_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b11"
+    top: "res4b12_branch2a"
+    name: "res4b12_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "bn4b12_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2a"
+    name: "scale4b12_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2a"
+    bottom: "res4b12_branch2a"
+    name: "res4b12_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2a"
+    top: "res4b12_branch2b"
+    name: "res4b12_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "bn4b12_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2b"
+    name: "scale4b12_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b12_branch2b"
+    bottom: "res4b12_branch2b"
+    name: "res4b12_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12_branch2b"
+    top: "res4b12_branch2c"
+    name: "res4b12_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "bn4b12_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12_branch2c"
+    top: "res4b12_branch2c"
+    name: "scale4b12_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b11"
+    bottom: "res4b12_branch2c"
+    top: "res4b12"
+    name: "res4b12"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b12"
+    name: "res4b12_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b12"
+    top: "res4b13_branch2a"
+    name: "res4b13_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "bn4b13_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2a"
+    name: "scale4b13_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2a"
+    bottom: "res4b13_branch2a"
+    name: "res4b13_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2a"
+    top: "res4b13_branch2b"
+    name: "res4b13_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "bn4b13_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2b"
+    name: "scale4b13_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b13_branch2b"
+    bottom: "res4b13_branch2b"
+    name: "res4b13_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13_branch2b"
+    top: "res4b13_branch2c"
+    name: "res4b13_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "bn4b13_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13_branch2c"
+    top: "res4b13_branch2c"
+    name: "scale4b13_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b12"
+    bottom: "res4b13_branch2c"
+    top: "res4b13"
+    name: "res4b13"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b13"
+    name: "res4b13_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b13"
+    top: "res4b14_branch2a"
+    name: "res4b14_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "bn4b14_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2a"
+    name: "scale4b14_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2a"
+    bottom: "res4b14_branch2a"
+    name: "res4b14_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2a"
+    top: "res4b14_branch2b"
+    name: "res4b14_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "bn4b14_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2b"
+    name: "scale4b14_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b14_branch2b"
+    bottom: "res4b14_branch2b"
+    name: "res4b14_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14_branch2b"
+    top: "res4b14_branch2c"
+    name: "res4b14_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "bn4b14_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14_branch2c"
+    top: "res4b14_branch2c"
+    name: "scale4b14_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b13"
+    bottom: "res4b14_branch2c"
+    top: "res4b14"
+    name: "res4b14"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b14"
+    name: "res4b14_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b14"
+    top: "res4b15_branch2a"
+    name: "res4b15_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "bn4b15_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2a"
+    name: "scale4b15_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2a"
+    bottom: "res4b15_branch2a"
+    name: "res4b15_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2a"
+    top: "res4b15_branch2b"
+    name: "res4b15_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "bn4b15_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2b"
+    name: "scale4b15_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b15_branch2b"
+    bottom: "res4b15_branch2b"
+    name: "res4b15_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15_branch2b"
+    top: "res4b15_branch2c"
+    name: "res4b15_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "bn4b15_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15_branch2c"
+    top: "res4b15_branch2c"
+    name: "scale4b15_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b14"
+    bottom: "res4b15_branch2c"
+    top: "res4b15"
+    name: "res4b15"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b15"
+    name: "res4b15_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b15"
+    top: "res4b16_branch2a"
+    name: "res4b16_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "bn4b16_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2a"
+    name: "scale4b16_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2a"
+    bottom: "res4b16_branch2a"
+    name: "res4b16_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2a"
+    top: "res4b16_branch2b"
+    name: "res4b16_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "bn4b16_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2b"
+    name: "scale4b16_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b16_branch2b"
+    bottom: "res4b16_branch2b"
+    name: "res4b16_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16_branch2b"
+    top: "res4b16_branch2c"
+    name: "res4b16_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "bn4b16_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16_branch2c"
+    top: "res4b16_branch2c"
+    name: "scale4b16_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b15"
+    bottom: "res4b16_branch2c"
+    top: "res4b16"
+    name: "res4b16"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b16"
+    name: "res4b16_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b16"
+    top: "res4b17_branch2a"
+    name: "res4b17_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "bn4b17_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2a"
+    name: "scale4b17_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2a"
+    bottom: "res4b17_branch2a"
+    name: "res4b17_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2a"
+    top: "res4b17_branch2b"
+    name: "res4b17_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "bn4b17_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2b"
+    name: "scale4b17_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b17_branch2b"
+    bottom: "res4b17_branch2b"
+    name: "res4b17_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17_branch2b"
+    top: "res4b17_branch2c"
+    name: "res4b17_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "bn4b17_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17_branch2c"
+    top: "res4b17_branch2c"
+    name: "scale4b17_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b16"
+    bottom: "res4b17_branch2c"
+    top: "res4b17"
+    name: "res4b17"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b17"
+    name: "res4b17_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b17"
+    top: "res4b18_branch2a"
+    name: "res4b18_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "bn4b18_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2a"
+    name: "scale4b18_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2a"
+    bottom: "res4b18_branch2a"
+    name: "res4b18_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2a"
+    top: "res4b18_branch2b"
+    name: "res4b18_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "bn4b18_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2b"
+    name: "scale4b18_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b18_branch2b"
+    bottom: "res4b18_branch2b"
+    name: "res4b18_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18_branch2b"
+    top: "res4b18_branch2c"
+    name: "res4b18_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "bn4b18_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18_branch2c"
+    top: "res4b18_branch2c"
+    name: "scale4b18_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b17"
+    bottom: "res4b18_branch2c"
+    top: "res4b18"
+    name: "res4b18"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b18"
+    name: "res4b18_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b18"
+    top: "res4b19_branch2a"
+    name: "res4b19_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "bn4b19_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2a"
+    name: "scale4b19_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2a"
+    bottom: "res4b19_branch2a"
+    name: "res4b19_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2a"
+    top: "res4b19_branch2b"
+    name: "res4b19_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "bn4b19_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2b"
+    name: "scale4b19_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b19_branch2b"
+    bottom: "res4b19_branch2b"
+    name: "res4b19_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19_branch2b"
+    top: "res4b19_branch2c"
+    name: "res4b19_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "bn4b19_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19_branch2c"
+    top: "res4b19_branch2c"
+    name: "scale4b19_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b18"
+    bottom: "res4b19_branch2c"
+    top: "res4b19"
+    name: "res4b19"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b19"
+    name: "res4b19_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b19"
+    top: "res4b20_branch2a"
+    name: "res4b20_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "bn4b20_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2a"
+    name: "scale4b20_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2a"
+    bottom: "res4b20_branch2a"
+    name: "res4b20_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2a"
+    top: "res4b20_branch2b"
+    name: "res4b20_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "bn4b20_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2b"
+    name: "scale4b20_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b20_branch2b"
+    bottom: "res4b20_branch2b"
+    name: "res4b20_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20_branch2b"
+    top: "res4b20_branch2c"
+    name: "res4b20_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "bn4b20_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20_branch2c"
+    top: "res4b20_branch2c"
+    name: "scale4b20_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b19"
+    bottom: "res4b20_branch2c"
+    top: "res4b20"
+    name: "res4b20"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b20"
+    name: "res4b20_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b20"
+    top: "res4b21_branch2a"
+    name: "res4b21_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "bn4b21_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2a"
+    name: "scale4b21_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2a"
+    bottom: "res4b21_branch2a"
+    name: "res4b21_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2a"
+    top: "res4b21_branch2b"
+    name: "res4b21_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "bn4b21_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2b"
+    name: "scale4b21_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b21_branch2b"
+    bottom: "res4b21_branch2b"
+    name: "res4b21_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21_branch2b"
+    top: "res4b21_branch2c"
+    name: "res4b21_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "bn4b21_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21_branch2c"
+    top: "res4b21_branch2c"
+    name: "scale4b21_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b20"
+    bottom: "res4b21_branch2c"
+    top: "res4b21"
+    name: "res4b21"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b21"
+    name: "res4b21_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b21"
+    top: "res4b22_branch2a"
+    name: "res4b22_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "bn4b22_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2a"
+    name: "scale4b22_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2a"
+    bottom: "res4b22_branch2a"
+    name: "res4b22_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2a"
+    top: "res4b22_branch2b"
+    name: "res4b22_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "bn4b22_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2b"
+    name: "scale4b22_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res4b22_branch2b"
+    bottom: "res4b22_branch2b"
+    name: "res4b22_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22_branch2b"
+    top: "res4b22_branch2c"
+    name: "res4b22_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "bn4b22_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22_branch2c"
+    top: "res4b22_branch2c"
+    name: "scale4b22_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b21"
+    bottom: "res4b22_branch2c"
+    top: "res4b22"
+    name: "res4b22"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res4b22"
+    name: "res4b22_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b22"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2a"
+    bottom: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5a_branch2b"
+    bottom: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2a"
+    bottom: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5b_branch2b"
+    bottom: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2a"
+    bottom: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    top: "res5c_branch2b"
+    bottom: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4b22"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rfcn_test.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rfcn_test.pt
new file mode 100644
index 000000000..f6a8a69d9
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rfcn_test.pt
@@ -0,0 +1,3699 @@
+name: "ResNet-50"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "rois"
+input_shape {
+  dim: 1 # to be changed on-the-fly to num ROIs
+  dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing
+}
+
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rpn_test.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rpn_test.pt
new file mode 100644
index 000000000..0a47c5362
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/rpn_test.pt
@@ -0,0 +1,2955 @@
+name: "ZF"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+#-----------------------layer +-------------------------
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#-----------------------output------------------------
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  top: 'scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt
new file mode 100644
index 000000000..8bfc7afe7
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_solver80k120k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
new file mode 100644
index 000000000..420e4a9c7
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rfcn_ohem_train.pt
@@ -0,0 +1,3781 @@
+name: "ResNet-50"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt
new file mode 100644
index 000000000..b8588d49b
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
new file mode 100644
index 000000000..4e0c2667b
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage1_rpn_train.pt
@@ -0,0 +1,2966 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt
new file mode 100644
index 000000000..59a946fc8
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_solver80k120k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rfcn_ohem"
+iter_size: 2
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
new file mode 100644
index 000000000..420e4a9c7
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rfcn_ohem_train.pt
@@ -0,0 +1,3781 @@
+name: "ResNet-50"
+layer {
+  name: 'data'
+  type: 'Python'
+  top: 'data'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt
new file mode 100644
index 000000000..a12082b25
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
new file mode 100644
index 000000000..a5cd0c60a
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage2_rpn_train.pt
@@ -0,0 +1,2966 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt
new file mode 100644
index 000000000..40a17b8f8
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_solver60k80k.pt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt"
+
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 60000
+display: 20
+average_loss: 100
+momentum: 0.9
+weight_decay: 0.0005
+
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rpn"
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
new file mode 100644
index 000000000..a5cd0c60a
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_alt_opt_5step_ohem/stage3_rpn_train.pt
@@ -0,0 +1,2966 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 256
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: "rpn_bbox_inside_weights"
+  bottom: "rpn_bbox_outside_weights"
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/test.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/test.prototxt
new file mode 100644
index 000000000..83b223c50
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/test.prototxt
@@ -0,0 +1,3787 @@
+name: "ResNet50"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res5c"
+  top: "rpn/output"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 4116 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 84
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+    name: "cls_prob"
+    type: "Softmax"
+    bottom: "cls_score"
+    top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 84
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/train_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/train_ohem.prototxt
new file mode 100644
index 000000000..f70910389
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/class-aware/train_ohem.prototxt
@@ -0,0 +1,3946 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res5c"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 4116 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 84
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver.prototxt
new file mode 100644
index 000000000..6fb18c2f1
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver.prototxt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rfcn"
+iter_size: 2
+# debug_info: true
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver_ohem.prototxt
new file mode 100644
index 000000000..8f4cad20b
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/solver_ohem.prototxt
@@ -0,0 +1,16 @@
+train_net: "models/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic_ohem.prototxt"
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 80000
+display: 20
+
+momentum: 0.9
+weight_decay: 0.0005
+# We disable standard caffe solver snapshotting and implement our own snapshot
+# function
+snapshot: 0
+# We still use the snapshot prefix, though
+snapshot_prefix: "resnet50_rfcn_ohem"
+iter_size: 2
+# debug_info: true
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/test_agnostic.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/test_agnostic.prototxt
new file mode 100644
index 000000000..6bb71ee32
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/test_agnostic.prototxt
@@ -0,0 +1,3787 @@
+name: "ResNet50"
+
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 224
+  dim: 224
+}
+
+input: "im_info"
+input_shape {
+  dim: 1
+  dim: 3
+}
+
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 decay_mult: 1.0 }
+  param { lr_mult: 2.0 decay_mult: 0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rois'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred_pre"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "cls_prob"
+   type: "Softmax"
+   bottom: "cls_score"
+   top: "cls_prob_pre"
+}
+
+layer {
+    name: "cls_prob_reshape"
+    type: "Reshape"
+    bottom: "cls_prob_pre"
+    top: "cls_prob"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 21
+        }
+    }
+}
+
+layer {
+    name: "bbox_pred_reshape"
+    type: "Reshape"
+    bottom: "bbox_pred_pre"
+    top: "bbox_pred"
+    reshape_param {
+        shape {
+            dim: -1
+            dim: 8
+        }
+    }
+}
+
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic.prototxt
new file mode 100644
index 000000000..48a47d1b3
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic.prototxt
@@ -0,0 +1,3877 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "loss_cls"
+   loss_weight: 1
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: 'bbox_inside_weights'
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+    name: "silence"
+    type: "Silence"
+    bottom: "bbox_outside_weights"
+}
+
diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic_ohem.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic_ohem.prototxt
new file mode 100644
index 000000000..2ce1c741c
--- /dev/null
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-50/rfcn_end2end/train_agnostic_ohem.prototxt
@@ -0,0 +1,3946 @@
+name: "ResNet-50"
+layer {
+  name: 'input-data'
+  type: 'Python'
+  top: 'data'
+  top: 'im_info'
+  top: 'gt_boxes'
+  python_param {
+    module: 'roi_data_layer.layer'
+    layer: 'RoIDataLayer'
+    param_str: "'num_classes': 21"
+  }
+}
+
+# ------------------------ conv1 -----------------------------
+layer {
+    bottom: "data"
+    top: "conv1"
+    name: "conv1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 7
+        pad: 3
+        stride: 2
+    }
+    param {
+        lr_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+    }
+    
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "bn_conv1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "scale_conv1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "conv1"
+    top: "conv1"
+    name: "conv1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv1"
+    top: "pool1"
+    name: "pool1"
+    type: "Pooling"
+    pooling_param {
+        kernel_size: 3
+        stride: 2
+        pool: MAX
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch1"
+    name: "res2a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "bn2a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    top: "res2a_branch1"
+    name: "scale2a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "pool1"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "bn2a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "scale2a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2a"
+    name: "res2a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2a"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "bn2a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "scale2a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2b"
+    name: "res2a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a_branch2b"
+    top: "res2a_branch2c"
+    name: "res2a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "bn2a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch2c"
+    top: "res2a_branch2c"
+    name: "scale2a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a_branch1"
+    bottom: "res2a_branch2c"
+    top: "res2a"
+    name: "res2a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2a"
+    name: "res2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "bn2b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "scale2b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2a"
+    name: "res2b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2a"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "bn2b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "scale2b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2b"
+    name: "res2b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b_branch2b"
+    top: "res2b_branch2c"
+    name: "res2b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "bn2b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b_branch2c"
+    top: "res2b_branch2c"
+    name: "scale2b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2a"
+    bottom: "res2b_branch2c"
+    top: "res2b"
+    name: "res2b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2b"
+    name: "res2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2b"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "bn2c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "scale2c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2a"
+    name: "res2c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2a"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 64
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "bn2c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "scale2c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2b"
+    name: "res2c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c_branch2b"
+    top: "res2c_branch2c"
+    name: "res2c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "bn2c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c_branch2c"
+    top: "res2c_branch2c"
+    name: "scale2c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2b"
+    bottom: "res2c_branch2c"
+    top: "res2c"
+    name: "res2c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res2c"
+    name: "res2c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch1"
+    name: "res3a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "bn3a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    top: "res3a_branch1"
+    name: "scale3a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res2c"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "bn3a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "scale3a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2a"
+    name: "res3a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2a"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "bn3a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "scale3a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2b"
+    name: "res3a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a_branch2b"
+    top: "res3a_branch2c"
+    name: "res3a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "bn3a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch2c"
+    top: "res3a_branch2c"
+    name: "scale3a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a_branch1"
+    bottom: "res3a_branch2c"
+    top: "res3a"
+    name: "res3a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3a"
+    name: "res3a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "bn3b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "scale3b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2a"
+    name: "res3b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2a"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "bn3b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "scale3b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2b"
+    name: "res3b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b_branch2b"
+    top: "res3b_branch2c"
+    name: "res3b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "bn3b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b_branch2c"
+    top: "res3b_branch2c"
+    name: "scale3b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3a"
+    bottom: "res3b_branch2c"
+    top: "res3b"
+    name: "res3b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3b"
+    name: "res3b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3b"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "bn3c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "scale3c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2a"
+    name: "res3c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2a"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "bn3c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "scale3c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2b"
+    name: "res3c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c_branch2b"
+    top: "res3c_branch2c"
+    name: "res3c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "bn3c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c_branch2c"
+    top: "res3c_branch2c"
+    name: "scale3c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3b"
+    bottom: "res3c_branch2c"
+    top: "res3c"
+    name: "res3c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3c"
+    name: "res3c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3c"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "bn3d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "scale3d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2a"
+    name: "res3d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2a"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 128
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "bn3d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "scale3d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2b"
+    name: "res3d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d_branch2b"
+    top: "res3d_branch2c"
+    name: "res3d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "bn3d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d_branch2c"
+    top: "res3d_branch2c"
+    name: "scale3d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3c"
+    bottom: "res3d_branch2c"
+    top: "res3d"
+    name: "res3d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res3d"
+    name: "res3d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch1"
+    name: "res4a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "bn4a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    top: "res4a_branch1"
+    name: "scale4a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res3d"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 2
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "bn4a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "scale4a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2a"
+    name: "res4a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2a"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "bn4a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "scale4a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2b"
+    name: "res4a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a_branch2b"
+    top: "res4a_branch2c"
+    name: "res4a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "bn4a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch2c"
+    top: "res4a_branch2c"
+    name: "scale4a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a_branch1"
+    bottom: "res4a_branch2c"
+    top: "res4a"
+    name: "res4a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4a"
+    name: "res4a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "bn4b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "scale4b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2a"
+    name: "res4b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2a"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "bn4b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "scale4b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2b"
+    name: "res4b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b_branch2b"
+    top: "res4b_branch2c"
+    name: "res4b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "bn4b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b_branch2c"
+    top: "res4b_branch2c"
+    name: "scale4b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4a"
+    bottom: "res4b_branch2c"
+    top: "res4b"
+    name: "res4b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4b"
+    name: "res4b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4b"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "bn4c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "scale4c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2a"
+    name: "res4c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2a"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "bn4c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "scale4c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2b"
+    name: "res4c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c_branch2b"
+    top: "res4c_branch2c"
+    name: "res4c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "bn4c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c_branch2c"
+    top: "res4c_branch2c"
+    name: "scale4c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4b"
+    bottom: "res4c_branch2c"
+    top: "res4c"
+    name: "res4c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4c"
+    name: "res4c_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4c"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "bn4d_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "scale4d_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2a"
+    name: "res4d_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2a"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "bn4d_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "scale4d_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2b"
+    name: "res4d_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d_branch2b"
+    top: "res4d_branch2c"
+    name: "res4d_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "bn4d_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d_branch2c"
+    top: "res4d_branch2c"
+    name: "scale4d_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4c"
+    bottom: "res4d_branch2c"
+    top: "res4d"
+    name: "res4d"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4d"
+    name: "res4d_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4d"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "bn4e_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "scale4e_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2a"
+    name: "res4e_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2a"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "bn4e_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "scale4e_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2b"
+    name: "res4e_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e_branch2b"
+    top: "res4e_branch2c"
+    name: "res4e_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "bn4e_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e_branch2c"
+    top: "res4e_branch2c"
+    name: "scale4e_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4d"
+    bottom: "res4e_branch2c"
+    top: "res4e"
+    name: "res4e"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4e"
+    name: "res4e_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4e"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "bn4f_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "scale4f_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2a"
+    name: "res4f_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2a"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 256
+        kernel_size: 3
+        pad: 1
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "bn4f_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "scale4f_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2b"
+    name: "res4f_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f_branch2b"
+    top: "res4f_branch2c"
+    name: "res4f_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "bn4f_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f_branch2c"
+    top: "res4f_branch2c"
+    name: "scale4f_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4e"
+    bottom: "res4f_branch2c"
+    top: "res4f"
+    name: "res4f"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res4f"
+    name: "res4f_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch1"
+    name: "res5a_branch1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "bn5a_branch1"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    top: "res5a_branch1"
+    name: "scale5a_branch1"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res4f"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "bn5a_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "scale5a_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2a"
+    name: "res5a_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2a"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "bn5a_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "scale5a_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2b"
+    name: "res5a_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a_branch2b"
+    top: "res5a_branch2c"
+    name: "res5a_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "bn5a_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch2c"
+    top: "res5a_branch2c"
+    name: "scale5a_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a_branch1"
+    bottom: "res5a_branch2c"
+    top: "res5a"
+    name: "res5a"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5a"
+    name: "res5a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "bn5b_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "scale5b_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2a"
+    name: "res5b_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2a"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "bn5b_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "scale5b_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2b"
+    name: "res5b_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b_branch2b"
+    top: "res5b_branch2c"
+    name: "res5b_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "bn5b_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b_branch2c"
+    top: "res5b_branch2c"
+    name: "scale5b_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5a"
+    bottom: "res5b_branch2c"
+    top: "res5b"
+    name: "res5b"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5b"
+    name: "res5b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5b"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "bn5c_branch2a"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "scale5c_branch2a"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2a"
+    name: "res5c_branch2a_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2a"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b"
+    type: "Convolution"
+    convolution_param {
+        num_output: 512
+        kernel_size: 3
+        dilation: 2
+        pad: 2
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "bn5c_branch2b"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "scale5c_branch2b"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2b"
+    name: "res5c_branch2b_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "res5c_branch2b"
+    top: "res5c_branch2c"
+    name: "res5c_branch2c"
+    type: "Convolution"
+    convolution_param {
+        num_output: 2048
+        kernel_size: 1
+        pad: 0
+        stride: 1
+        bias_term: false
+    }
+    param {
+        lr_mult: 1.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "bn5c_branch2c"
+    type: "BatchNorm"
+    batch_norm_param {
+        use_global_stats: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5c_branch2c"
+    top: "res5c_branch2c"
+    name: "scale5c_branch2c"
+    type: "Scale"
+    scale_param {
+        bias_term: true
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+    param {
+        lr_mult: 0.0
+        decay_mult: 0.0
+    }
+}
+
+layer {
+    bottom: "res5b"
+    bottom: "res5c_branch2c"
+    top: "res5c"
+    name: "res5c"
+    type: "Eltwise"
+}
+
+layer {
+    bottom: "res5c"
+    top: "res5c"
+    name: "res5c_relu"
+    type: "ReLU"
+}
+
+
+#========= RPN ============
+
+layer {
+  name: "rpn_conv/3x3"
+  type: "Convolution"
+  bottom: "res4f"
+  top: "rpn/output"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 512
+    kernel_size: 3 pad: 1 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+layer {
+  name: "rpn_relu/3x3"
+  type: "ReLU"
+  bottom: "rpn/output"
+  top: "rpn/output"
+}
+
+layer {
+  name: "rpn_cls_score"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_cls_score"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 18   # 2(bg/fg) * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+  name: "rpn_bbox_pred"
+  type: "Convolution"
+  bottom: "rpn/output"
+  top: "rpn_bbox_pred"
+  param { lr_mult: 1.0 }
+  param { lr_mult: 2.0 }
+  convolution_param {
+    num_output: 36   # 4 * 9(anchors)
+    kernel_size: 1 pad: 0 stride: 1
+    weight_filler { type: "gaussian" std: 0.01 }
+    bias_filler { type: "constant" value: 0 }
+  }
+}
+
+layer {
+   bottom: "rpn_cls_score"
+   top: "rpn_cls_score_reshape"
+   name: "rpn_cls_score_reshape"
+   type: "Reshape"
+   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'rpn-data'
+  type: 'Python'
+  bottom: 'rpn_cls_score'
+  bottom: 'gt_boxes'
+  bottom: 'im_info'
+  bottom: 'data'
+  top: 'rpn_labels'
+  top: 'rpn_bbox_targets'
+  top: 'rpn_bbox_inside_weights'
+  top: 'rpn_bbox_outside_weights'
+  python_param {
+    module: 'rpn.anchor_target_layer'
+    layer: 'AnchorTargetLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+layer {
+  name: "rpn_loss_cls"
+  type: "SoftmaxWithLoss"
+  bottom: "rpn_cls_score_reshape"
+  bottom: "rpn_labels"
+  propagate_down: 1
+  propagate_down: 0
+  top: "rpn_cls_loss"
+  loss_weight: 1
+  loss_param {
+    ignore_label: -1
+    normalize: true
+  }
+}
+
+layer {
+  name: "rpn_loss_bbox"
+  type: "SmoothL1Loss"
+  bottom: "rpn_bbox_pred"
+  bottom: "rpn_bbox_targets"
+  bottom: 'rpn_bbox_inside_weights'
+  bottom: 'rpn_bbox_outside_weights'
+  top: "rpn_loss_bbox"
+  loss_weight: 1
+  smooth_l1_loss_param { sigma: 3.0 }
+}
+
+#========= RoI Proposal ============
+
+layer {
+  name: "rpn_cls_prob"
+  type: "Softmax"
+  bottom: "rpn_cls_score_reshape"
+  top: "rpn_cls_prob"
+}
+
+layer {
+  name: 'rpn_cls_prob_reshape'
+  type: 'Reshape'
+  bottom: 'rpn_cls_prob'
+  top: 'rpn_cls_prob_reshape'
+  reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
+}
+
+layer {
+  name: 'proposal'
+  type: 'Python'
+  bottom: 'rpn_cls_prob_reshape'
+  bottom: 'rpn_bbox_pred'
+  bottom: 'im_info'
+  top: 'rpn_rois'
+#  top: 'rpn_scores'
+  python_param {
+    module: 'rpn.proposal_layer'
+    layer: 'ProposalLayer'
+    param_str: "'feat_stride': 16"
+  }
+}
+
+#layer {
+#  name: 'debug-data'
+#  type: 'Python'
+#  bottom: 'data'
+#  bottom: 'rpn_rois'
+#  bottom: 'rpn_scores'
+#  python_param {
+#    module: 'rpn.debug_layer'
+#    layer: 'RPNDebugLayer'
+#  }
+#}
+
+layer {
+  name: 'roi-data'
+  type: 'Python'
+  bottom: 'rpn_rois'
+  bottom: 'gt_boxes'
+  top: 'rois'
+  top: 'labels'
+  top: 'bbox_targets'
+  top: 'bbox_inside_weights'
+  top: 'bbox_outside_weights'
+  python_param {
+    module: 'rpn.proposal_target_layer'
+    layer: 'ProposalTargetLayer'
+    param_str: "'num_classes': 2"
+  }
+}
+
+#----------------------new conv layer------------------
+layer {
+    bottom: "res5c"
+    top: "conv_new_1"
+    name: "conv_new_1"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1024
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "conv_new_1"
+    name: "conv_new_1_relu"
+    type: "ReLU"
+}
+
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_cls"
+    name: "rfcn_cls"
+    type: "Convolution"
+    convolution_param {
+        num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+layer {
+    bottom: "conv_new_1"
+    top: "rfcn_bbox"
+    name: "rfcn_bbox"
+    type: "Convolution"
+    convolution_param {
+        num_output: 392 #8*(7^2) cls_num*(score_maps_size^2)
+        kernel_size: 1
+        pad: 0
+        weight_filler {
+            type: "gaussian"
+            std: 0.01
+        }
+        bias_filler {
+            type: "constant"
+            value: 0
+        }
+    }
+    param {
+        lr_mult: 1.0
+    }
+    param {
+        lr_mult: 2.0
+    }
+}
+
+#--------------position sensitive RoI pooling--------------
+layer {
+    bottom: "rfcn_cls"
+    bottom: "rois"
+    top: "psroipooled_cls_rois"
+    name: "psroipooled_cls_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 21
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_cls_rois"
+    top: "cls_score"
+    name: "ave_cls_score_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+layer {
+    bottom: "rfcn_bbox"
+    bottom: "rois"
+    top: "psroipooled_loc_rois"
+    name: "psroipooled_loc_rois"
+    type: "PSROIPooling"
+    psroi_pooling_param {
+        spatial_scale: 0.0625
+        output_dim: 8
+        group_size: 7
+    }
+}
+
+layer {
+    bottom: "psroipooled_loc_rois"
+    top: "bbox_pred"
+    name: "ave_bbox_pred_rois"
+    type: "Pooling"
+    pooling_param {
+        pool: AVE
+        kernel_size: 7
+        stride: 7
+    }
+}
+
+
+#--------------online hard example mining--------------
+layer {
+   name: "per_roi_loss_cls"
+   type: "SoftmaxWithLossOHEM"
+   bottom: "cls_score"
+   bottom: "labels"
+   top: "temp_loss_cls"
+   top: "temp_prob_cls"
+   top: "per_roi_loss_cls"
+   loss_weight: 0
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_inside_weights"
+   top: "temp_loss_bbox"
+   top: "per_roi_loss_bbox"
+   loss_weight: 0
+   loss_weight: 0
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "per_roi_loss"
+   type: "Eltwise"
+   bottom: "per_roi_loss_cls"
+   bottom: "per_roi_loss_bbox"
+   top: "per_roi_loss"
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   bottom: "rois"
+   bottom: "per_roi_loss"
+   bottom: "labels"
+   bottom: "bbox_inside_weights"
+   top: "labels_ohem"
+   top: "bbox_loss_weights_ohem"
+   name: "annotator_detector"
+   type: "BoxAnnotatorOHEM"
+   box_annotator_ohem_param {
+        roi_per_img: 128
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "silence"
+   type: "Silence"
+   bottom: "bbox_outside_weights"
+   bottom: "temp_loss_cls"
+   bottom: "temp_prob_cls"
+   bottom: "temp_loss_bbox"
+}
+
+#-----------------------output------------------------
+layer {
+   name: "loss"
+   type: "SoftmaxWithLoss"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "loss_cls"
+   loss_weight: 1
+   loss_param {
+        ignore_label: -1
+   }
+   propagate_down: true
+   propagate_down: false
+}
+
+layer {
+   name: "accuarcy"
+   type: "Accuracy"
+   bottom: "cls_score"
+   bottom: "labels_ohem"
+   top: "accuarcy"
+   #include: { phase: TEST }
+   accuracy_param {
+        ignore_label: -1
+   }
+   propagate_down: false
+   propagate_down: false
+}
+
+layer {
+   name: "loss_bbox"
+   type: "SmoothL1LossOHEM"
+   bottom: "bbox_pred"
+   bottom: "bbox_targets"
+   bottom: "bbox_loss_weights_ohem"
+   top: "loss_bbox"
+   loss_weight: 1
+   loss_param {
+        normalization: PRE_FIXED
+        pre_fixed_normalizer: 128
+   }
+   propagate_down: true
+   propagate_down: false
+   propagate_down: false
+}
+
diff --git a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/deploy.prototxt b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/deploy.prototxt
index 733229e89..0ebace1e7 100644
--- a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/deploy.prototxt
+++ b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/deploy.prototxt
@@ -1613,14 +1613,15 @@ layer {
       nms_threshold: 0.45
       top_k: 400
     }
-    save_output_param {
-      output_directory: "data/ssd_out/VOC2007/SSD_300x300"
-      output_name_prefix: "comp4_det_test_"
-      output_format: "VOC"
-      label_map_file: "data/VOC0712/labelmap_voc.prototxt"
-      name_size_file: "data/VOC0712/test_name_size.txt"
-      num_test_image: 4952
-    }
+    #Only for output to disk
+    #save_output_param {
+    #  output_directory: "data/ssd_out/VOC2007/SSD_300x300"
+    #  output_name_prefix: "comp4_det_test_"
+    #  output_format: "VOC"
+    #  label_map_file: "data/VOC0712/labelmap_voc.prototxt"
+    #  name_size_file: "data/VOC0712/test_name_size.txt"
+    #  num_test_image: 4952
+    #}
     code_type: CENTER_SIZE
     keep_top_k: 200
     confidence_threshold: 0.01
diff --git a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt
index a9d4e83a9..9781bcc05 100644
--- a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt
+++ b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt
@@ -1637,14 +1637,15 @@ layer {
       nms_threshold: 0.45
       top_k: 400
     }
-    save_output_param {
-      output_directory: "data/ssd_out/VOC2007/SSD_300x300"
-      output_name_prefix: "comp4_det_test_"
-      output_format: "VOC"
-      label_map_file: "data/VOC0712/labelmap_voc.prototxt"
-      name_size_file: "data/VOC0712/test_name_size.txt"
-      num_test_image: 4952
-    }
+    #Only for output to disk
+    #save_output_param {
+    #  output_directory: "data/ssd_out/VOC2007/SSD_300x300"
+    #  output_name_prefix: "comp4_det_test_"
+    #  output_format: "VOC"
+    #  label_map_file: "data/VOC0712/labelmap_voc.prototxt"
+    #  name_size_file: "data/VOC0712/test_name_size.txt"
+    #  num_test_image: 4952
+    #}
     code_type: CENTER_SIZE
     keep_top_k: 200
     confidence_threshold: 0.01
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 34c939a5b..296bfad4f 100755
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -36,6 +36,11 @@
 #
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
 from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
+try:
+    from ._caffe import MultiSync, MultiSolver
+except ImportError:
+    pass
+
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 533c67295..2de7611ee 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -59,6 +59,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 #ifdef USE_MLSL
 #include "caffe/multinode/mlsl.hpp"
+#include "caffe/multinode/multi_sync.hpp"
 #endif
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
@@ -321,6 +322,7 @@ struct NdarrayCallPolicies : public bp::default_call_policies {
   }
 };
 
+
 bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
   if (bp::len(kwargs) > 0) {
     throw std::runtime_error("Blob.reshape takes no kwargs");
@@ -371,6 +373,23 @@ void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
   solver->add_callback(new PythonCallback<Dtype>(on_start, on_gradients_ready));
 }
 
+#ifdef USE_MLSL
+template<typename Dtype>
+void MultiSolverBackward(MultiSolver<Dtype> * solver)
+{
+  solver->Backward();
+}
+
+#ifdef FW_OVERLAP_OPT
+template<typename Dtype>
+Dtype MultiSolverUpdateAndForward(MultiSolver<Dtype> * solver)
+{
+  return solver->UpdateAndForward();
+}
+#endif
+
+#endif
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -381,6 +400,19 @@ BOOST_PYTHON_MODULE(_caffe) {
 
 #ifdef USE_MLSL
   InitMultinode();
+  bp::class_<MultiSolver<Dtype>, shared_ptr<MultiSolver<Dtype> >, boost::noncopyable>(
+    "MultiSolver", bp::init<shared_ptr<Solver<Dtype>>>())
+    .def("update", &MultiSolver<Dtype>::WaitAndUpdate)
+#ifdef FW_OVERLAP_OPT
+    .def("update_and_forward", &MultiSolverUpdateAndForward<Dtype>)
+#endif
+    .def("forward", &MultiSolver<Dtype>::Forward)
+    .def("backward", &MultiSolverBackward<Dtype>)
+    .def("clear_param_diffs", &MultiSolver<Dtype>::ClearParamDiffs);
+
+  bp::class_<MultiSync<Dtype>> ( "MultiSync", bp::init<shared_ptr<Solver<Dtype>>>())
+     .def("init", &MultiSync<Dtype>::init)
+     .add_property("solver", &MultiSync<Dtype>::get_solver);
 #endif
   bp::def("_node_id", &NodeId);
   bp::def("_num_nodes", &NumNodes);
@@ -462,7 +494,14 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
-          NdarrayCallPolicies()));
+          NdarrayCallPolicies()))
+#ifdef CO_SIM
+    .add_property("data_ro",     bp::make_function(&Blob<Dtype>::cpu_data_cosim,
+          NdarrayCallPolicies()))
+    .add_property("diff_ro",     bp::make_function(&Blob<Dtype>::cpu_diff_cosim,
+          NdarrayCallPolicies()))
+#endif
+    ;
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
 
   bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
diff --git a/scripts/benchmark_config_default.json b/scripts/benchmark_config_default.json
new file mode 100644
index 000000000..daa0d46f8
--- /dev/null
+++ b/scripts/benchmark_config_default.json
@@ -0,0 +1,44 @@
+{
+    "params":{
+        "topology" : "resnet_50",
+        "hostfile" : "",
+        "network" : "",
+        "netmask" : "",
+        "dummy_data_use" : true,
+        "scal_test" : false,
+        "caffe_bin" : "",
+        "engine" : ""
+    },
+    "perf_batch_size_table" : {
+        "alexnet" : {
+            "bdw" : "1024",
+            "skx" : "1024",
+            "knl" : "512",
+            "knm" : "1024"
+        },
+        "googlenet" : {
+            "bdw" : "128",
+            "skx" : "144",
+            "knl" : "144",
+            "knm" : "144"
+        },
+        "googlenet_v2" : {
+            "bdw" : "128",
+            "skx" : "192",
+            "knl" : "192",
+            "knm" : "128"
+        },
+        "resnet_50" : {
+            "bdw" : "32",
+            "skx" : "96",
+            "knl" : "64",
+            "knm" : "64"
+        }
+    },
+    "scal_batch_size_table" : {
+        "alexnet" : "256",
+        "googlenet" : "64",
+        "googlenet_v2" : "64",
+        "resnet_50" : "64"
+    }
+} 
diff --git a/scripts/benchmark_config_template.json b/scripts/benchmark_config_template.json
index 440939c62..f1d141ff1 100644
--- a/scripts/benchmark_config_template.json
+++ b/scripts/benchmark_config_template.json
@@ -9,7 +9,7 @@
         "caffe_bin" : "",
         "engine" : "choose CAFFE, MKL2017 or MKLDNN"
     },
-    "batch_size_table" : {
+    "perf_batch_size_table" : {
         "alexnet" : {
             "bdw" : "1024",
             "skx" : "1024",
@@ -34,5 +34,11 @@
             "knl" : "64",
             "knm" : "64"
         }
+    },
+    "scal_batch_size_table" : {
+        "alexnet" : "256",
+        "googlenet" : "64",
+        "googlenet_v2" : "64",
+        "resnet_50" : "64"
     }
 } 
diff --git a/scripts/build_intelcaffe.sh b/scripts/build_intelcaffe.sh
index a29da73dc..f16acebab 100755
--- a/scripts/build_intelcaffe.sh
+++ b/scripts/build_intelcaffe.sh
@@ -6,7 +6,7 @@ function usage
     echo "Usage:"
     echo "  $script_name [--multinode] [--compiler icc/gcc] [--rebuild] "
     echo "               [--boost_root boost_install_dir] [--layer_timing]"
-    echo "               [--debug]"
+    echo "               [--debug] [--build_option]"
     echo ""
     echo "  Parameters:"
     echo "    multinode:    specify it to build caffe for multinode. build for single"
@@ -20,6 +20,7 @@ function usage
     echo "    layer_timing: build caffe for multinode with CAFFE_PER_LAYER_TIMINGS flag."
     echo "                  by default, the flag is NOT included for build."
     echo "    debug:        build caffe with debug flag. by default, the option is off."
+    echo "    build_option: build option to disable optimization. by default, the option is blank."
 }
 
 function check_dependency
@@ -36,15 +37,18 @@ function check_dependency
 function build_caffe_gcc
 {
     is_multinode_=$1
-
+    build_option_=$2
     cp Makefile.config.example Makefile.config
 
     if [ $is_multinode_ -eq 1 ]; then
         echo "USE_MLSL := 1" >> Makefile.config
+        echo "ALLOW_LMDB_NOLOCK := 1" >> Makefile.config
 
-        mlslvars_sh=`find external/mlsl/ -name mlslvars.sh`
-        if [ -f $mlslvars_sh ]; then
-            source $mlslvars_sh
+        if [ -z $MLSL_ROOT ]; then
+            mlslvars_sh=`find external/mlsl/ -name mlslvars.sh`
+            if [ -f $mlslvars_sh ]; then
+                source $mlslvars_sh
+            fi
         fi
     fi
 
@@ -59,12 +63,19 @@ function build_caffe_gcc
     if [ "$boost_root" != "" ]; then
         echo "BOOST_ROOT := $boost_root" >> Makefile.config
     fi
-
+    for option in $build_option_
+    do
+        grep "$option\s*:= 0" Makefile.config > /dev/null
+        if [ $? -eq 0 ]; then
+           sed -i "s/$option\s*:= 0/$option := 1/g" Makefile.config
+        fi
+    done
     if [ $is_rebuild -eq 1 ]; then
         make clean
     fi
-
-    make -j 8
+    
+    make -j $(nproc)
+    make pycaffe
 }
 
 function download_build_boost
@@ -98,9 +109,10 @@ function download_build_boost
 function build_caffe_icc
 {
     is_multinode_=$1
+    build_option_=$2
     cmake_params="-DCPU_ONLY=1 -DBOOST_ROOT=$boost_root"
     if [ $is_multinode_ -eq 1 ]; then
-        cmake_params+=" -DUSE_MLSL=1"
+        cmake_params+=" -DUSE_MLSL=1 -DALLOW_LMDB_NOLOCK=1"
     fi
 
     if [ $is_layer_timing -eq 1 ]; then
@@ -110,7 +122,10 @@ function build_caffe_icc
     if [ $debug -eq 1 ]; then
         cmake_params+=" -DDEBUG=1"
     fi
-
+    for option in $build_option_
+    do
+        cmake_params+=" -D$option=1 "
+    done
     build_dir=$root_dir/build
     if [ $is_rebuild -eq 1 ] && [ -d $build_dir ]; then
         rm -r $build_dir
@@ -121,19 +136,22 @@ function build_caffe_icc
 
     echo "Parameters: $cmake_params"
     CC=icc CXX=icpc cmake .. $cmake_params
-    CC=icc CXX=icpc make all -j 8
+    CC=icc CXX=icpc make all -j $(nproc) 
 }
 
 function sync_caffe_dir
 {
-  caffe_dir=`pwd`
-  caffe_parent_dir=`dirname $caffe_dir`
-  which ansible >/dev/null
-  if [ $? -eq 0 ]; then
-      ansible ourcluster -m synchronize -a "src=$caffe_dir dest=$caffe_parent_dir"
-  else
-      echo "Warning: no ansible command for synchronizing caffe directory in nodes"
-  fi
+    echo "Synchronize caffe binary between nodes..."
+    caffe_dir=`pwd`
+    caffe_parent_dir=`dirname $caffe_dir`
+    which ansible >/dev/null
+    if [ $? -eq 0 ]; then
+        set -x
+        ansible ourcluster -m synchronize -a "src=$caffe_dir dest=$caffe_parent_dir"
+        set +x
+    else
+        echo "Warning: no ansible command for synchronizing caffe directory in nodes"
+    fi
 }
 
 
@@ -145,6 +163,7 @@ boost_root=""
 is_rebuild=0
 compiler="icc"
 is_multinode=0
+build_option=""
 while [[ $# -ge 1 ]]
 do
     key="$1"
@@ -163,6 +182,10 @@ do
             boost_root="$2"
             shift
             ;;
+        --build_option)
+            build_option="$2"
+            shift
+            ;;
         --layer_timing)
             is_layer_timing=1
             ;;
@@ -202,6 +225,12 @@ do
     fi
 done
 
+# Fix the compilation failure if icc environment is set.
+# During building caffe, MKL library will be downloaded,
+# and the environment variable will be set.
+unset MKLROOT
+unset CPATH
+
 echo "Build Intel Caffe by $compiler..."
 if [ "$compiler" == "icc" ]; then
     if [ "$boost_root" == "" ]; then
@@ -212,12 +241,12 @@ if [ "$compiler" == "icc" ]; then
         boost_root=$(cd $boost_root; pwd)
     fi
 
-    build_caffe_icc $is_multinode
+    build_caffe_icc $is_multinode "$build_option"
 else
-    build_caffe_gcc $is_multinode
+    build_caffe_gcc $is_multinode "$build_option"
 fi
 
 if [ $is_multinode -eq 1 ]; then
-  sync_caffe_dir
+    sync_caffe_dir
 fi
 
diff --git a/scripts/calibrator.py b/scripts/calibrator.py
index 4bda3bb8d..b6317fc7b 100644
--- a/scripts/calibrator.py
+++ b/scripts/calibrator.py
@@ -39,12 +39,6 @@
 import sys
 import copy
 import argparse
-
-caffe_root = "../"
-sys.path.insert(0, caffe_root + 'python')
-
-import caffe
-from caffe.proto import caffe_pb2
 import google.protobuf.text_format as txtf
 
 
@@ -207,7 +201,6 @@ def transform_convolutions(model_path, compiled_model_path):
                                               if (l, index) != convolution_layers[-1]
                                               else len(net.layer), [i[0] for i in fusion_layer])
         inputwith_relu = get_all_top_layers(l, net, index, skip_layers, interesting_layers)
-
         for si in range(0, len(new_net.layer[index].quantization_param.scale_out)):
             if len(outputwith_relu) > 0 or l.name in u8_layers or conv_relu_flag:  # u8
                 new_net.layer[index].quantization_param.scale_out[si] = round(u8_max / new_net.layer[index].
@@ -233,11 +226,10 @@ def transform_convolutions(model_path, compiled_model_path):
 
 
 def generate_sample(sample_path, input_model, weights,
-                    quantized_model, detection, iterations=1, error_margin=1, power=0):
-    cmd = '{0} quantize -model {1} -weights {2} -model_quantized {3} -iterations {4} ' \
-          '-trimming_mode dynamic_fixed_point -error_margin {5} -power {6}'.format(sample_path, input_model, weights,
-                                                                                   quantized_model, iterations,
-                                                                                   error_margin, power)
+                    quantized_model, detection, scaling_mode, iterations=1, error_margin=1, power=0):
+    cmd = '{0} quantize -model {1} -weights {2} -model_quantized {3} -iterations {4} -error_margin {5} -power {6}' \
+          ' -scaling {7} -trimming_mode dynamic_fixed_point'.format(sample_path, input_model, weights, quantized_model,
+                                                                    iterations, error_margin, power, scaling_mode)
     if detection:
         cmd += ' --detection=1'
 
@@ -307,6 +299,20 @@ def tuning_quantized_topology(base_top1_accuracy, prototxt, caffe_bin, model_wei
         current_top1_accuracy = get_the_accuracy(caffe_bin, prototxt, model_weights, iterations, detection, blob_name)
 
 
+def accuracy_blob_name_parser(prototxt):
+    net = read_prototxt(prototxt)
+    if not net:
+        print 'Please check the model prototxt integrity.'
+        sys.exit(-1)
+    res = {}
+    for i in net.layer:
+        if i.type == 'Accuracy':
+            if i.HasField('accuracy_param'):
+                res[i.accuracy_param.top_k] = i.top[0]
+            else:
+                res[1] = i.top[0]
+    return res[sorted(res.keys())[0]] if res else ''
+
 def check_blob_name_existence(prototxt, blob_name):
     net = read_prototxt(prototxt)
     if not net.layer:
@@ -322,15 +328,17 @@ def check_blob_name_existence(prototxt, blob_name):
 
 if __name__ == '__main__':
     usage_string = 'Usage: 1.Build the caffe\n ' \
-                   '2.cd /path/to/caffe/scripts\n ' \
-                   '3.python calibrator.py ' \
-                   ' -r /path/to/caffe/build ' \
-                   ' -w pre-trained-fp32 weights ' \
-                   ' -m typology ' \
-                   ' -i iterations ' \
-                   ' -l acceptable accuracy loss value, the default value is 0.01(stands for 1%)' \
-                   ' -d 1(0 means classification while 1 means detection, the default value is 0' \
-                   ' -n blob name which means accuracy.\n '
+                    '2.cd /path/to/caffe/scripts\n ' \
+                    '3.python calibrator.py ' \
+                    ' -r /path/to/caffe/build ' \
+                    ' -w pre-trained-fp32 weights ' \
+                    ' -m typology ' \
+                    ' -i iterations ' \
+                    ' -l acceptable accuracy loss value, the default value is 0.01 stands for one percent' \
+                    ' -d 1(0 means classification while 1 means detection, the default value is 0' \
+                    ' -n blob name which means accuracy' \
+                    ' -c scaling mode, the default value is single' \
+                    ' -s sampling iterations'
 
     parser = argparse.ArgumentParser(add_help=False)
     parser.add_argument('-h', '--help', action='help', help=usage_string)
@@ -346,7 +354,7 @@ def check_blob_name_existence(prototxt, blob_name):
 
     parser.add_argument('-l', '--accuracy_loss', action='store', dest='loss', default=0.01,
                         help='the acceptable accuracy loss that raised by 8-Bit quantization, '
-                             'default value is 0.01(1%).')
+                             'default value is 0.01(one percent).')
 
     parser.add_argument('-d', '--detection', action='store', dest='is_detection', default=0,
                         help='0 for classification while 1 for detection, default value is 0.')
@@ -356,13 +364,49 @@ def check_blob_name_existence(prototxt, blob_name):
 
     parser.add_argument('-n', '--blob_name', action='store', dest='blob_name', default='',
                         help='top blob name which stands for accuracy')
-    params = parser.parse_args()
 
+    parser.add_argument('-c', '--weights_channel', action='store', dest='scaling_mode', default='single',
+                        help='the scaling mode for weights')
+    
+    parser.add_argument('-s', '--sampling_iterations', action='store', dest='sampling_iterations', default=10,
+                        help='iteration number of sampling, the default value is 10.')
+
+    params = parser.parse_args()
+    
+    if not check_existence(params.root):
+        print 'Please check the {} existence.'.format(params.root)
+        sys.exit(-1)
+    
+    pycaffe_path = os.path.abspath(os.path.dirname(os.path.abspath(params.root))) + os.path.sep + 'python'
+    if not check_existence(pycaffe_path):
+        print "Please check the pycaffe existence.Suggest to rebuild pycaffe via 'make pycaffe'"
+    sys.path.insert(0, pycaffe_path)
+    import caffe
+    from caffe.proto import caffe_pb2
+    
     try:
         user_input_iterations = int(params.iterations)
     except:
         print 'Set the iterations to the default value 1000'
         user_input_iterations = 1000
+    else:
+        if user_input_iterations < 1:
+            print 'Invalid iterations!The value should be larger than zero.'
+            sys.exit(-1)
+    try:
+        user_sampling_iteration = int(params.sampling_iterations)
+    except:
+        print 'Set the sampling iteration to the default value 10'
+        user_sampling_iteration = 10
+    else:
+        if user_sampling_iteration < 1:
+            print 'Invalid sampling iteration!The value should be larger than zero.'
+            sys.exit(-1)
+
+    if params.scaling_mode != 'multipe' and params.scaling_mode != 'single':
+        user_scaling_mode = 'single'
+    else:
+        user_scaling_mode = params.scaling_mode
 
     try:
         toleration = float(params.loss)
@@ -379,34 +423,47 @@ def check_blob_name_existence(prototxt, blob_name):
         detection_flag = 0
 
     model = os.path.abspath(params.model)
+    if not check_existence(model):
+        print 'Please check model: {} existence.'.format(model)
+        sys.exit(-1)
+
     user_input_weights = os.path.abspath(params.weights)
-    sample = os.path.abspath(params.root + 'tools/sample')
-    caffe_bin_path = os.path.abspath(params.root + 'tools/caffe')
-    setup_env()
+    if not check_existence(user_input_weights):
+        print 'Please check weights: {} existence.'.format(user_input_weights)
+        sys.exit(-1)
+
+    sample = os.path.abspath(params.root + os.path.sep + 'tools/sample')
+    if not check_existence(sample):
+        print 'Please check sample: {} existence.'.format(sample)
+        sys.exit(-1)
 
-    if not check_existence(model) or not check_existence(user_input_weights) or not check_existence(sample) \
-            or not check_existence(caffe_bin_path):
+    caffe_bin_path = os.path.abspath(params.root + os.path.sep + 'tools/caffe')
+    if not check_existence(caffe_bin_path):
         print 'Please check model/weights/sample existence.'
         sys.exit(-1)
 
+    setup_env()
+
     target_blob_name = params.blob_name
+    if not target_blob_name and not detection_flag:
+        target_blob_name = accuracy_blob_name_parser(model)
+
     if not target_blob_name or not check_blob_name_existence(model, target_blob_name):
         print 'Please specify valid blob name and rerun the script.'
         sys.exit(-1)
 
-    sys.path.insert(0, params.root + '../python')
     quantized_prototxt = model.rsplit('.')[0] + '_quantized.prototxt'
     quantized_weights = user_input_weights.rsplit('.')[0] + '_quantized.caffemodel'
-    enable_floating_point = 0
+    enable_power_of_2 = 0
     print 'Sampling...'
-    generate_sample(sample, model, user_input_weights,
-                    quantized_prototxt, detection_flag, 10, 100 * toleration, enable_floating_point)
+    generate_sample(sample, model, user_input_weights, quantized_prototxt, detection_flag, user_scaling_mode,
+                    user_sampling_iteration, 100 * toleration, enable_power_of_2)
     print 'Sampling done'
     print 'Generating the FP32 accuracy...'
     top_1 = get_the_accuracy(caffe_bin_path, model, user_input_weights, user_input_iterations, detection_flag,
                              target_blob_name)
     print 'FP32 accuracy is: {}'.format(top_1)
     tuning_quantized_topology(top_1, quantized_prototxt, caffe_bin_path, user_input_weights, user_input_iterations,
-                              enable_floating_point, toleration, detection_flag, target_blob_name)
+                              enable_power_of_2, toleration, detection_flag, target_blob_name)
 
     print 'Updated prototxt {} is generated.'.format(quantized_prototxt)
diff --git a/scripts/install_deps.sh b/scripts/install_deps.sh
index 0bf6e3f5b..b3bab9bb3 100755
--- a/scripts/install_deps.sh
+++ b/scripts/install_deps.sh
@@ -24,7 +24,7 @@ function usage
 function check_os
 {
     echo "Check OS and the version..."
-    
+
     centos_ver_file="/etc/centos-release"
     if [ -f $centos_ver_file ]; then
         os="centos"
@@ -65,11 +65,11 @@ function is_sudoer
 
 function install_python_deps
 {
-    eval $command_prefix pip install --upgrade pip
+    pip install --user --upgrade pip
     pushd $root_dir/python >/dev/null
     for req in $(cat requirements.txt) pydot;
     do
-        eval $command_prefix pip install $req
+        pip install --user $req
     done
     popd >/dev/null
 }
@@ -101,11 +101,11 @@ function install_deps
 
 function install_python_deps_multinode
 {
-    ansible all -m shell -a "$command_prefix pip install --upgrade pip"
+    ansible all -m shell -a "pip install --user --upgrade pip"
     pushd $root_dir/python >/dev/null
     for req in $(cat requirements.txt) pydot;
     do
-        ansible all -m shell -a "$command_prefix pip install $req"
+        ansible all -m shell -a "pip install --user $req"
     done
     popd >/dev/null
 
@@ -152,7 +152,7 @@ function install_deps_multinode
     ansible ourcluster -m ping
 
     if [ "$os" == "centos" ]; then
-    ansible all -m shell -a "$package_installer install python-devel boost boost-devel numpy numpy-devel gflags gflags-devel glog glog-devel protobuf protobuf-devel hdf5 hdf5-devel lmdb lmdb-devel leveldb leveldb-devel snappy-devel opencv opencv-devel scipy"
+        ansible all -m shell -a "$package_installer install python-devel boost boost-devel numpy numpy-devel gflags gflags-devel glog glog-devel protobuf protobuf-devel hdf5 hdf5-devel lmdb lmdb-devel leveldb leveldb-devel snappy-devel opencv opencv-devel scipy"
     elif [ "$os" == "ubuntu" ]; then
         ansible all -m shell -a "$package_installer install pkg-config libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler"
         ansible all -m shell -a "$package_installer install --no-install-recommends libboost-all-dev"
diff --git a/scripts/obtain_optimal_batch_size.py b/scripts/obtain_optimal_batch_size.py
new file mode 100644
index 000000000..bbb5407e6
--- /dev/null
+++ b/scripts/obtain_optimal_batch_size.py
@@ -0,0 +1,159 @@
+import subprocess
+import argparse
+import re
+import os
+import time
+import logging
+
+class OptimalBS(object):
+    '''Search for optimal batch size on a specified model file'''
+    def __init__(self, main_args):
+        '''init func'''
+        self.model_file = main_args.model_file
+        self.min_bs = main_args.min_bs
+        self.max_bs = main_args.max_bs
+        self.iterations = main_args.iterations
+        self.dummy_use = main_args.dummy_data_use
+        current_time = time.strftime("%Y%m%d%H%M%S")
+        logging.basicConfig(filename = 'result-optimal_batch_size-{}.log'.format(current_time),level = logging.INFO)
+        self.check_parameters()
+
+    def check_parameters(self):
+        '''check if parameters available'''
+        if not os.path.isfile(self.model_file):
+            logging.exception('model file {} does not exist, please specify it.'.format(model_file))
+        if self.min_bs > self.max_bs:
+            logging.exception('max bs: {} smaller than min bs: {}.'.format(max_bs, min_bs))
+        
+    def _gen_new_model_file_name(self, new_bs):
+        '''obtain new model file name'''
+        model_path = os.path.dirname(self.model_file)
+        model_file_name = os.path.basename(self.model_file)
+        new_model_file_name = '_'.join([new_bs, model_file_name])
+        new_model_file = '/'.join([model_path, new_model_file_name])
+        return new_model_file
+        
+    def gen_model_with_new_bs(self, new_bs):
+        '''return model file with new batch size'''
+        new_bs = str(new_bs)
+        return self._gen_new_dummy_model(new_bs) if self.dummy_use else self._gen_new_real_model(new_bs)
+
+    def _gen_new_real_model(self, new_bs):
+        '''return new model with real data input'''
+        new_model_file = self._gen_new_model_file_name(new_bs)
+        batch_size_pattern = re.compile("^\s+batch_size:.*")
+        bn_stats_batch_size_pattern = re.compile(".*bn_stats_batch_size:.*")
+        # we only care about train phase batch size 
+        batch_size_cnt = 1
+        with open(self.model_file, 'r') as src_f, open(new_model_file, 'w') as dst_f:
+            cnt = 0
+            for line in src_f.readlines():
+                if re.match(batch_size_pattern, line) and cnt < batch_size_cnt:
+                    line = re.sub('[0-9]+', new_bs, line, count = 1)
+                    cnt += 1
+                #consider bn_stats_batch_size field
+                if re.match(bn_stats_batch_size_pattern, line):
+                    line = re.sub('[0-9]+', new_bs, line, count = 1)
+                dst_f.write(line)
+        if cnt < batch_size_cnt:
+            logging.exception("Error: can't find batch size pattern within the model file {}.".format(self.model_file))
+        return new_model_file
+
+    def _gen_new_dummy_model(self, new_bs):
+        '''return new model with dummy data input'''
+        new_model_file = self._gen_new_model_file_name(new_bs)
+        shape_pattern = re.compile(".*shape:.*")
+        dim_pattern = re.compile(".*dim:.*")
+        # we only care about train phase batch size 
+        shape_cnt = 2
+        dim_cnt = 0
+        with open(self.model_file, 'r') as src_f, open(new_model_file, 'w') as dst_f:
+            cnt = 0
+            for line in src_f.readlines():
+                if re.match(shape_pattern, line):
+                    cnt += 1
+                    dim_cnt = 0
+                if re.match(dim_pattern, line) and cnt <= shape_cnt and dim_cnt == 0:
+                    line = re.sub('[0-9]+', new_bs, line, count = 1)
+                    dim_cnt += 1
+                dst_f.write(line)
+        if cnt < shape_cnt:
+            logging.exception("Error: can't find batch size pattern within the model file {}.".format(self.model_file))
+        return new_model_file
+    
+    def calculate_fps(self, train_log_file, bs):
+        '''calculate fps on train logs generated by model file with new bs'''
+        if not os.path.isfile(train_log_file):
+            logging.exception("Error: traing log file {} does not exist...".format(traing_log_file))
+        average_time = ''
+        with open(train_log_file, 'r') as f:
+            average_fwd_bwd_time_pattern = re.compile(".*Average Forward-Backward:.*")
+            for line in f.readlines():
+                if re.match(average_fwd_bwd_time_pattern, line):
+                    average_time = line.split()[-2]
+                    break
+        if average_time == "": 
+            logging.exception("Error: can't find average forward-backward time within logs, please check logs under: {}".format(train_log_file))
+        average_time = float(average_time)
+        speed = float(bs) * 1000.0 / average_time
+        logging.info("bs: {}, benchmark speed: {} images/sec".format(str(bs), str(speed)))
+        return speed
+    
+    def exec_command_and_show(self, exec_command):
+        '''execute shell command and print it out'''
+        def _exec_command_and_iter_show(cmd):
+            out = subprocess.Popen(cmd, shell = True, stdin = subprocess.PIPE, stdout = subprocess.PIPE, stderr = subprocess.PIPE, universal_newlines = True)
+            for stdout_line in iter(out.stdout.readline, ''):
+                yield stdout_line
+            return_code = out.wait()
+            if return_code:
+                raise subprocess.CalledProcessError(return_code, cmd)
+        for line in _exec_command_and_iter_show(exec_command):
+            print line
+    
+    def obtain_fps(self, bs):
+        '''obtain fps on model_file with new batch size'''
+        caffe_root = os.path.dirname(os.path.dirname(__file__))
+        caffe_tool = caffe_root + 'build/tools/caffe'
+        mode = 'time'
+        current_time = time.strftime("%Y%m%d%H%M%S")
+        train_log_file = 'result-train-bs{}-{}.log'.format(str(bs), current_time)
+        new_model_file = self.gen_model_with_new_bs(bs)
+        exec_command = ' '.join([caffe_tool, mode, '--model', new_model_file, '--iterations', str(self.iterations)])
+        exec_command += ' 2>&1 | tee {}'.format(train_log_file)
+        self.exec_command_and_show(exec_command)
+        fps = self.calculate_fps(train_log_file, bs)
+        os.remove(train_log_file)
+        os.remove(new_model_file)
+        return fps
+    
+    def find_optimal_bs(self):
+        '''find optimal bs for model file and return it'''
+        optimal_bs, max_fps = self.min_bs, self.obtain_fps(self.min_bs)
+        for bs in xrange(self.min_bs + 1, self.max_bs + 1):
+            fps = self.obtain_fps(bs)
+            if fps > max_fps:
+                optimal_bs, max_fps = bs, fps
+        return optimal_bs
+
+def parse_args():
+    '''parse arguments'''
+    description = 'Used to obtain optimal batch size for specified model file.'
+    arg_parser = argparse.ArgumentParser(description = description)
+    arg_parser.add_argument('--model_file', help = 'model prototxt file you specified')
+    arg_parser.add_argument('--min_bs', type = int, help = 'min batch size you expect')
+    arg_parser.add_argument('--max_bs', type = int, help = 'max batch size you expect')
+    arg_parser.add_argument('--iterations', type = int, default = 100, help = 'how many iterations to run for caffe time')
+    arg_parser.add_argument('--dummy_data_use', action = 'store_true',  help = "dummy_data_use, if set 'True', will use dummy data as input; else if set 'False', will use other data source like lmdb for neural network as input")
+    args = arg_parser.parse_args()
+    return args
+
+def main():
+    '''main routine'''
+    main_args = parse_args()
+    optimal_bs_finder = OptimalBS(main_args)
+    optimal_bs = optimal_bs_finder.find_optimal_bs()
+    logging.info('optimal batch size is: {}'.format(optimal_bs))
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/run_benchmark.py b/scripts/run_benchmark.py
index 3da1f5434..576f684b8 100755
--- a/scripts/run_benchmark.py
+++ b/scripts/run_benchmark.py
@@ -7,38 +7,39 @@
 import socket
 import logging
 
-topology_list = ["alexnet", "googlenet", "googlenet_v2", "resnet_50", 'all']
 class CaffeBenchmark(object):
     '''Used to do caffe benchmarking'''
-    def __init__(self, topology, bkm_batch_size, host_file = "", network = "", tcp_netmask = "", engine = "", dummy_data_use = True, caffe_bin = "", scal_test = True):
+    def __init__(self, bench_params):
         '''params initialization'''
-        self.topology = topology
-        self.host_file = host_file
-        self.network = network
-        self.tcp_netmask = tcp_netmask
-        self.dummy_data_use = dummy_data_use
-        self.engine = engine
-        self.caffe_bin = caffe_bin
-        self.scal_test = scal_test
+        self.topology = bench_params.topology
+        self.host_file = bench_params.host_file
+        self.network = bench_params.network
+        self.tcp_netmask = bench_params.tcp_netmask
+        self.dummy_data_use = bench_params.dummy_data_use
+        self.engine = bench_params.engine
+        self.caffe_bin = bench_params.caffe_bin
+        self.scal_test = bench_params.scal_test
         self.num_nodes = 1
-        self.cpu_model = "skx"
+        self.cpu_model = 'skx'
         # flag used to mark if we have detected which cpu model we're using
         self.unknown_cpu = False
         self.iterations = 100
         self.caffe_root = os.path.dirname(os.path.dirname(__file__))
         # model template path
-        self.model_path = self.caffe_root + "models/intel_optimized_models"
+        self.model_path = os.path.join(self.caffe_root, "models/intel_optimized_models/benchmark")
         # specific script used to run intelcaffe 
-        self.caffe_run_script = self.caffe_root + "scripts/run_intelcaffe.sh"
-        self.bkm_batch_size = bkm_batch_size
+        self.caffe_run_script = os.path.join(self.caffe_root, "scripts/run_intelcaffe.sh")
+        self.bkm_batch_size = bench_params.bkm_batch_size
+        self.support_topologies = self.bkm_batch_size.keys()
+        self.support_topologies.append('all')
         self.check_parameters()
         current_time = time.strftime("%Y%m%d%H%M%S")
         logging.basicConfig(filename = 'result-benchmark-{}.log'.format(current_time),level = logging.INFO)
 
     def is_supported_topology(self):
         '''check if input topology is supported'''
-        if self.topology not in topology_list:
-            logging.exception("The topology you specified as {} is not supported. Supported topologies are {}".format(self.topology, topology_list))
+        if self.topology not in self.support_topologies:
+            logging.exception("The topology you specified as {} is not supported. Supported topologies are {}".format(self.topology, self.support_topologies))
     
     def calculate_numnodes(self):
         '''calculate current using nodes'''
@@ -65,7 +66,6 @@ def _exec_command_and_iter_show(cmd):
         for line in _exec_command_and_iter_show(cmd):
             print line
         
-    
     def detect_cpu(self):
         '''check which IA platform currently using'''
         command_name = "lscpu | grep 'Model name' | awk -F ':' '{print $2}'"
@@ -88,21 +88,21 @@ def detect_cpu(self):
             self.unknown_cpu = True
             logging.info("Can't detect which cpu model currently using, will use default settings, which may not be the optimal one.")
     
-    def obtain_model_file(self, model):
-        '''change original model file batch size to bkm batch size'''
+    def gen_model_file(self, model):
+        '''generate model file with new batch size which equal to bkm batch size'''
         prototxt_file = "train_val_dummydata.prototxt" if self.dummy_data_use else "train_val.prototxt"
-        dst_model_file = "/".join([self.model_path, model, '-'.join([self.cpu_model, prototxt_file])])
+        dst_model_file = os.path.join(self.model_path, model, '-'.join([self.cpu_model, prototxt_file]))
         if os.path.isfile(dst_model_file):
             os.remove(dst_model_file)
-        src_model_file = "/".join([self.model_path, model, prototxt_file])
+        src_model_file = os.path.join(self.model_path, model, prototxt_file)
         if not os.path.isfile(src_model_file):
             logging.exception("template model file {} doesn't exist.".format(src_model_file))
         batch_size_pattern = re.compile(".*shape:.*") if self.dummy_data_use else re.compile("^\s+batch_size:.*")
         # we only care about train phase batch size for benchmarking
         batch_size_cnt = 2 if self.dummy_data_use else 1
-        if model not in self.bkm_batch_size or self.cpu_model not in self.bkm_batch_size[model]:
+        if model not in self.bkm_batch_size or not self.scal_test and self.cpu_model not in self.bkm_batch_size[model]:
             logging.exception("Can't find batch size of topology {} and cpu model {} within batch size table".format(model, self.cpu_model))
-        new_batch_size = self.bkm_batch_size[model][self.cpu_model]
+        new_batch_size = self.bkm_batch_size[model] if self.scal_test else self.bkm_batch_size[model][self.cpu_model]
         with open(src_model_file, 'r') as src_f, open(dst_model_file, 'w') as dst_f:
             cnt = 0
             for line in src_f.readlines():
@@ -113,16 +113,16 @@ def obtain_model_file(self, model):
                 dst_f.write(line) 
         return dst_model_file
 
-    def obtain_solver_file(self, model):
-        '''obtain suitable solver file for training benchmark'''
+    def gen_solver_file(self, model):
+        '''generate suitable solver file for training benchmark'''
         solver_prototxt_file = "solver_dummydata.prototxt" if self.dummy_data_use else "solver.prototxt"
-        dst_solver_file = "/".join([self.model_path, model, '-'.join([self.cpu_model, solver_prototxt_file])])
+        dst_solver_file = os.path.join(self.model_path, model, '-'.join([self.cpu_model, solver_prototxt_file]))
         if os.path.isfile(dst_solver_file):
             os.remove(dst_solver_file)
-        src_solver_file = "/".join([self.model_path, model, solver_prototxt_file])
+        src_solver_file = os.path.join(self.model_path, model, solver_prototxt_file)
         if not os.path.isfile(src_solver_file):
             logging.exception("template solver file {} doesn't exist.".format(src_solver_file))
-        dst_model_file = self.obtain_model_file(model)
+        dst_model_file = self.gen_model_file(model)
         max_iter = "200"
         display_iter = "1"
         net_path_pattern = re.compile(".*net:.*")
@@ -144,10 +144,10 @@ def run_specific_model(self, model):
         '''run the topology you specified'''
         self.calculate_numnodes()
         if self.num_nodes == 1:
-            model_file = self.obtain_model_file(model)
+            model_file = self.gen_model_file(model)
             exec_command = ' '.join([self.caffe_run_script, '--model_file', model_file, '--mode time', '--iteration', str(self.iterations), '--benchmark none'])
         else:
-            solver_file = self.obtain_solver_file(model)
+            solver_file = self.gen_solver_file(model)
             exec_command = ' '.join([self.caffe_run_script, '--hostfile', self.host_file, '--solver', solver_file, '--network', self.network, '--benchmark none'])
             if self.network == "tcp":
                 exec_command += " --netmask {}".format(self.tcp_netmask)
@@ -183,7 +183,7 @@ def obtain_intelcaffe_log(self):
             caffe_log_file = "-".join(["outputCluster", self.cpu_model, str(self.num_nodes) + '.txt'])
         else:
             caffe_log_file = "-".join(["outputCluster", "unknown", str(self.num_nodes) + '.txt'])
-        intelcaffe_log = "/".join([result_dir, caffe_log_file])
+        intelcaffe_log = os.path.join(result_dir, caffe_log_file)
         logging.info('intelcaffe log: %s' % intelcaffe_log)
         return intelcaffe_log
     
@@ -226,12 +226,11 @@ def obtain_batch_size(self):
         if not os.path.isfile(log_file):
             logging.exception("Error: log file {} does not exist...".format(log_file))
         with open(log_file, 'r') as f:
-            batch_size_pattern_time = re.compile(".*SetMinibatchSize.*")
             batch_size_pattern_dummy = re.compile(".*dim:.*")
-            batch_size_pattern_real = re.compile("^\s+batch_size:.*")
+            batch_size_pattern_real = re.compile(".*\s+batch_size:.*")
             batch_size = ''
             for line in f.readlines():
-                if re.match(batch_size_pattern_time, line) or re.match(batch_size_pattern_real, line) or re.match(batch_size_pattern_dummy, line):
+                if re.match(batch_size_pattern_real, line) or re.match(batch_size_pattern_dummy, line):
                     batch_size = line.split()[-1]
                     break
         if batch_size == '':
@@ -276,9 +275,8 @@ def manipulate_host_file(self):
                 self.hosts[0], self.hosts[index] = self.hosts[index], self.hosts[0]
                 break
         
-        
-    def obtain_host_file(self, num_nodes):
-        '''obtain suitable host file to do scaling test'''
+    def gen_host_file(self, num_nodes):
+        '''generate suitable host file to do scaling test'''
         dst_host_file = 'scal_hostfile'
         with open(dst_host_file, 'w') as dst_f:
             for i in xrange(num_nodes):
@@ -294,7 +292,7 @@ def run_scal_test(self, model):
         origin_hostfile = self.host_file
         fps_table = {}
         while num_nodes > 0:
-            self.host_file = self.obtain_host_file(num_nodes)
+            self.host_file = self.gen_host_file(num_nodes)
             self.run_specific_model(model)
             fps_table[num_nodes] = self.speed
             num_nodes /= 2
@@ -320,7 +318,7 @@ def run_benchmark(self):
         self.detect_cpu()
         logging.info("Cpu model: {}".format(self.model_string))
         if self.topology == 'all':
-            for model in topology_list:
+            for model in self.support_topologies:
                 if model == 'all':
                     continue
                 logging.info("--{}".format(model))
@@ -343,26 +341,39 @@ def check_parameters(self):
             if self.network == "tcp" and self.tcp_netmask == "":
                 logging.exception("Error: need to specify tcp network's netmask")
 
+class BenchmarkParams(object):
+    '''encapsulate benchmark parameters here'''
+    def __init__(self, config_file):
+        '''initialize benchmark parameters through a config file'''
+        if config_file == '' or not os.path.isfile(config_file):
+            logging.exception("Cant't find config file {}.".format(config_file))
+        with open(config_file, 'r') as f:
+            try:
+                config = json.load(f)
+            except Exception:
+                logging.exception("Error: check if your json config file is correct.")
+            self.topology = config['params']['topology']
+            self.host_file = config['params']['hostfile']
+            self.network = config['params']['network']
+            self.tcp_netmask = config['params']['netmask']
+            self.engine = config['params']["engine"]
+            self.dummy_data_use = config['params']['dummy_data_use']
+            self.scal_test = config['params']['scal_test']
+            self.caffe_bin = config['params']['caffe_bin']
+            self.bkm_batch_size = config['scal_batch_size_table'] if self.scal_test else config['perf_batch_size_table']
+        
+def parse_args():
+    '''parse arguments'''
+    description = 'Used to run intelcaffe throughput performance or scaling efficiency benchmarking.'
+    arg_parser = argparse.ArgumentParser(description = description)
+    arg_parser.add_argument('-c', '--configfile', default = 'scripts/benchmark_config_default.json', help = "config file which contains the parameters you want and the batch size you want to use on all topologies and platforms. Please check https://github.com/intel/caffe/wiki/Run-benchmark to see how to use it, default is 'scripts/benchmark_config_default.json'")
+    return arg_parser.parse_args()
+
 def main():
     '''main routine'''
-    arg_parser = argparse.ArgumentParser(description = "Used to run intelcaffe training benchmark")
-    arg_parser.add_argument('--configfile', help = "config file which contains the parameters you want and the batch size you want to use on all topologies and platforms")
-    main_args = arg_parser.parse_args()
-    if main_args.configfile == '' or not os.path.isfile(main_args.configfile):
-        logging.exception("Cant't find config file {}.".format(main_args.configfile))
-    with open(main_args.configfile, 'r') as f:
-        config = json.load(f)
-        topology = config['params']['topology']
-        host_file = config['params']['hostfile']
-        network = config['params']['network']
-        tcp_netmask = config['params']['netmask']
-        engine = config['params']["engine"]
-        dummy_data_use = config['params']['dummy_data_use']
-        scal_test = config['params']['scal_test']
-        caffe_bin = config['params']['caffe_bin']
-        bkm_batch_size = config['batch_size_table']
-    
-    caffe_benchmark = CaffeBenchmark(topology, bkm_batch_size, host_file, network, tcp_netmask, engine, dummy_data_use, caffe_bin, scal_test)
+    main_args = parse_args()
+    bench_params = BenchmarkParams(main_args.configfile) 
+    caffe_benchmark = CaffeBenchmark(bench_params)
     caffe_benchmark.run_benchmark()
 
 if __name__ == "__main__":
diff --git a/scripts/run_benchmark.sh b/scripts/run_benchmark.sh
deleted file mode 100755
index 9ee320236..000000000
--- a/scripts/run_benchmark.sh
+++ /dev/null
@@ -1,327 +0,0 @@
-#!/bin/bash
-
-# model path
-model_path="models/intel_optimized_models"
-
-# network topology, support alexnet, googlenet, googlenet v2, resnet50
-# if you set it as 'all', then it will benchmark all supported topologies.
-topology=""
-topology_list="alexnet googlenet googlenet_v2 resnet_50 all"
-declare -a model_list=("alexnet" "googlenet" "googlenet_v2" "resnet_50")
-
-# it's assigned by detect_cpu
-cpu_model="skx"
-
-# flag used to mark if we have detected which cpu model we're using
-unknown_cpu=0
-
-# specify default engine for running caffe benchmarks
-engine=""
-
-# default support single node
-numnodes=1
-
-# intelcaffe_log_file obtain outputs of 'run_intelcaffe'
-intelcaffe_log_file=""
-
-# specific script used to run intelcaffe 
-caffe_run_script="./scripts/run_intelcaffe.sh"
-
-# path of caffe binary
-caffe_bin=""
-
-# iterations to run benchmark
-iterations=100
-
-# hostfile needed to run multinodes mode benchmark
-host_file=""
-
-# network parameters
-network="opa"
-tcp_netmask=""
-
-
-data_source="dummy"
-
-function usage
-{
-    script_name=$0
-    echo "Usage:"
-    echo "   $script_name --topology network_topology [--hostfile host_file] [--network opa/tcp] [--netmask tcp_netmask]"
-    echo "                [--data_source lmdb/dummy] [--caffe_bin caffe_bin_path] [--engine engine]"
-    echo ""
-    echo "   Parameters:"
-    echo "     topology: network topology used to benchmark, support alexnet, googlenet, googlenet_v2, resnet_50"
-    echo "               , by specifying it as 'all', we run all supported topologies."
-    echo "     hostfile: host_file needed in multinodes mode, should contain list of nodes ips or hostnames"
-    echo "     network: opa(default), tcp, used in multinodes mode to specify the network type"
-    echo "     netmask: only used if network is tcp, set as the net work card name within your network"
-    echo "     data_source: dummy(default), lmdb. data source for neural network."
-    echo "     caffe_bin_path: specify path of caffe binary."
-    echo "     engine: empty value(default), MKL2017 and MKLDNN. Default empty value"
-    echo "             is to use default engine in Intel Caffe."
-    echo ""
-}
-
-function is_supported_topology
-{
-    echo ""
-    if [[ "$topology_list" =~ (^|[[:space:]])"$topology"($|[[:space:]]) ]]; then
-        echo "Topology: ${topology}"
-    else
-        echo "$topology is not supported, please check the usage." 
-        usage 
-        exit 1
-    fi
-}
-
-function calculate_numnodes
-{
-    if [[ $host_file != "" ]]; then
-        host_list=(`cat $host_file | sort | uniq`)
-        numnodes=${#host_list[@]}
-        if [ $numnodes -eq 0 ]; then
-            echo "Error: empty host list. Exit."
-            exit 1
-        fi
-    fi
-    echo "Number of nodes: $numnodes"
-}
-
-function detect_cpu
-{
-    # detect cpu model
-    model_string=`lscpu | grep "Model name" | awk -F ':' '{print $2}'`
-    if [[ $model_string == *"Phi"* ]]; then
-        if [[ $model_string =~ 72(1|3|5|9)0 ]]; then
-            cpu_model="knl"
-        elif [[ $model_string == *"72"* ]]; then
-            cpu_model="knm"
-        else
-            unknown_cpu=1
-            echo "Can't detect which cpu model currently using, will use default settings, which may not be the optimal one."
-        fi
-    else
-        model_num=`echo $model_string | awk '{print $4}'`
-        if [[ $model_num =~ ^[8|6|5|4|3]1 ]]; then
-            cpu_model="skx"
-        elif [[ $model_num =~ ^E5-[4|2|1]6|^E7-[8|4]8|^E3-12|^D[-]?15 ]]; then
-            cpu_model="bdw"
-        else
-            unknown_cpu=1
-            echo "Can't detect which cpu model currently using, will use default settings, which may not be the optimal one."
-        fi
-    fi
-}
-
-function run_specific_model
-{
-    if [ $numnodes -eq 1 ]; then
-        if [ "$data_source" == "dummy" ]; then
-            model_file="models/intel_optimized_models/${model}/${cpu_model}/train_val_dummydata.prototxt"
-        else
-            model_file="models/intel_optimized_models/${model}/${cpu_model}/train_val.prototxt"
-        fi
-        exec_command="${caffe_run_script} --model_file ${model_file} --mode time --iteration ${iterations} --benchmark none"
-    else
-        if [ "$data_source" == "dummy" ]; then
-            solver_file="models/intel_optimized_models/${model}/${cpu_model}/solver_dummydata.prototxt"
-        else
-            solver_file="models/intel_optimized_models/${model}/${cpu_model}/solver.prototxt"
-        fi
-
-        exec_command="${caffe_run_script} --hostfile $host_file --solver $solver_file --network $network --benchmark none"
-        if [ $network == "tcp" ]; then
-            exec_command+=" --netmask $tcp_netmask"
-        fi
-    fi 
-    
-    if [ "$engine" != "" ]; then
-        exec_command+=" --engine $engine"
-    fi
-    if [ "$caffe_bin" != "" ]; then
-        exec_command+=" --caffe_bin $caffe_bin"
-    fi
-
-    # Result file to save detailed run intelcaffe results
-    if [ $unknown_cpu -eq 0 ]; then
-        result_log_file="result-${cpu_model}-${model}-`date +%Y%m%d%H%M%S`.log"
-    else
-        result_log_file="result-unknown-${model}-`date +%Y%m%d%H%M%S`.log"
-    fi
-    $exec_command 2>&1 | tee  $result_log_file
-    obtain_intelcaffe_log $result_log_file
-    calculate_images_per_second $intelcaffe_log_file 
-}
-
-function obtain_intelcaffe_log
-{
-    echo "Result_log_file : $1"
-    if [ -f $1 ]; then
-       result_dir_line=`cat $1 | grep "Result folder:"`
-       if [[ result_dir_line = "" ]]; then
-           echo "Couldn't find result folder within file $1"
-           exit 1
-       fi
-       result_dir=`echo $result_dir_line | awk -F ' ' '{print $(NF)}'`
-       if [ $unknown_cpu -eq 0 ]; then
-           caffe_log_file="outputCluster-${cpu_model}-${numnodes}.txt"
-       else
-           caffe_log_file="outputCluster-unknown-${numnodes}.txt"
-       fi
-       intelcaffe_log_file="${result_dir}/${caffe_log_file}"
-    else
-       echo "Couldn't see result log file $result_log_file"
-       exit 1
-    fi
-}
-
-function obtain_average_fwd_bwd_time
-{
-    result_file=$1
-    if [ ! -f $result_file ]; then
-        echo "Error: result file $result_file does not exist..."
-        exit 1
-    fi
-
-    if [ $numnodes -eq 1 ]; then
-        average_time_line=`cat $result_file | grep "Average Forward-Backward"`
-        if [ "$average_time_line" = "" ]; then
-            echo "running intelcaffe failed, please check logs under: $result_file"
-            exit 1
-        fi
-        average_time=`echo $average_time_line | awk -F ' ' '{print $(NF-1)}'`
-    else
-        start_iteration=100
-        iteration_num=100
-        total_time=0
-        deltaTimeList=`cat $result_file | grep "DELTA TIME" | tail -n "+${start_iteration}" | head -n ${iteration_num} | awk '{print $(NF-1)}'`
-        if [ "$deltaTimeList" = "" ]; then
-            echo "running intelcaffe failed, please check logs under: $result_file"
-            exit 1
-        fi
-        
-        for delta_time in ${deltaTimeList}
-        do
-            iteration_time=`echo "$delta_time" | bc`
-            total_time=`echo "$total_time+$iteration_time" | bc`
-        done
-
-        average_time=`echo "$total_time*1000/$iteration_num" | bc`
-    fi
-    echo "average time: ${average_time}"
-}
-
-function obtain_batch_size
-{
-    log_file=$1
-    if [ ! -f $log_file ]; then
-        echo "Error: log file $log_file does not exist..."
-        exit 1
-    fi
-    batch_size=`cat $log_file | grep shape | sed -n "3, 1p" | awk '{print $(NF-4)}'`
-    batch_size_opt=`cat $log_file | grep SetMinibatchSize | sed -n "1, 1p" | awk '{print $(NF)}'`
-    if [[ $batch_size_opt != "" ]]; then
-        batch_size=$batch_size_opt
-    fi
-    echo "batch size: $batch_size"
-}
-
-function calculate_images_per_second 
-{
-    obtain_batch_size $1
-    obtain_average_fwd_bwd_time $1
-    if [ $numnodes -eq 1 ]; then
-        speed=`echo "$batch_size*1000/$average_time" | bc`
-    else
-        speed=`echo "$batch_size*$numnodes*1000/$average_time" | bc`
-    fi
-    echo "benchmark speed : $speed images/sec"
-}
-
-
-function run_benchmark
-{
-    detect_cpu
-    calculate_numnodes
-    echo "Cpu model : $model_string"
-    if [[ $topology = "all" ]]; then
-       for ((i=0; i<${#model_list[@]}; i++))
-       do
-          echo "--${model_list[$i]}"
-          model=${model_list[$i]}
-          run_specific_model
-       done
-    else
-       model=$topology
-       run_specific_model
-    fi 
-}
-
-function check_parameters
-{
-    if [[ $topology = "" ]]; then
-        echo "Error: topology is not specified."
-        usage
-        exit 1
-    fi
-    
-    if [[ $host_file != "" ]]; then
-        if [ "$network" = "tcp" -a "$tcp_netmask" = "" ]; then
-            echo "Error: need to specify tcp network's netmask"
-            usage
-            exit 1
-        fi
-    fi
-    is_supported_topology
-}
-
-if [[ $# -le 1 ]]; then
-    usage
-    exit 0
-fi
-
-while [[ $# -ge 1 ]]
-do
-    key="$1"
-    case $key in 
-        --topology)
-            topology="$2"
-            shift
-            ;;
-        --hostfile)
-            host_file="$2"
-            shift
-            ;;
-        --network)
-            network="$2"
-            shift
-            ;;
-        --netmask)
-            tcp_netmask="$2"
-            shift
-            ;;
-        --engine)
-            engine="$2"
-            shift
-            ;;
-        --data_source)
-            data_source="$2"
-            shift
-            ;;
-        --caffe_bin)
-            caffe_bin=$2
-            shift
-            ;;
-        *)
-            echo "Unknown option: $key"
-            usage
-            exit 1
-            ;;
-    esac
-    shift
-done
-
-check_parameters
-
-run_benchmark
diff --git a/scripts/run_intelcaffe.sh b/scripts/run_intelcaffe.sh
index 1761f6e92..c2d80a9bf 100755
--- a/scripts/run_intelcaffe.sh
+++ b/scripts/run_intelcaffe.sh
@@ -55,6 +55,10 @@ mpibench_param="allreduce"
 script_dir=$(dirname $0)
 
 caffe_bin=""
+msg_priority="off"
+msg_priority_threshold=10000
+
+mpi_iallreduce_algo=""
 
 function usage
 {
@@ -73,6 +77,8 @@ function usage
     echo "               [--mpibench_param mpibench_param]"
     echo "               [--caffe_bin  caffe_binary_path]"
     echo "               [--cpu cpu_model]"
+    echo "               [--msg_priority on/off] [--msg_priority_threshold 10000]"
+    echo "               [--mpi_iallreduce_algo mpi_iallreduce_algo]"
     echo ""
     echo "  Parameters:"
     echo "    hostfile: host file includes list of nodes. Only used if you're running with multinode"
@@ -98,6 +104,9 @@ function usage
     echo "               included in supported list. Value: bdw, knl, skx and knm."
     echo "               bdw - Broadwell, knl - Knights Landing, skx - Skylake,"
     echo "               knm - Knights Mill."
+    echo "    msg_priority: off (default). Enable/disable message priority in MLSL."
+    echo "    msg_priority_threshold: 10000 (default), if message priority is on."
+    echo "    mpi_iallreduce_algo: adjust MPI iallreduce algorithms for synchronizing gradients in nodes."
     echo ""
 }
 
@@ -105,37 +114,63 @@ declare -a cpu_list=("Intel Xeon E7-88/48xx, E5-46/26/16xx, E3-12xx, D15/D-15 (B
                      "Intel Xeon Phi 7210/30/50/90 (Knights Landing)" 
                      "Intel Xeon Platinum 81/61/51/41/31xx (Skylake)")
 
+# the flag is true, while the machine to execute this script is one of machines included in hostfile
+# otherwise, we need to run caffe on remote machine by mpirun command
+is_local_run=1
+
+# check localhost is included in hostfile
+function is_localhost_in_hostfile
+{
+    local_hostname=`hostname`
+
+    host_file_=$1
+    if [ "$host_file_" != "" ]; then
+        host_list=( `cat $host_file_ | sort -V | uniq ` )
+        for host in ${host_list[@]}
+        do
+            hostname=`ssh $host "hostname"`
+            if [ $hostname == $local_hostname ]; then
+                return 1
+            fi
+        done
+        return 0
+    fi
+    
+    return 1
+}
+
 function detect_cpu
 {
     # detect cpu model
     model_string=`lscpu | grep "Model name" | awk -F ':' '{print $2}'`
+    cpu_model_="skx"
+
     if [[ $model_string == *"Phi"* ]]; then
         if [[ $model_string =~ 72(1|3|5|9)0 ]]; then
-            cpu_model="knl"
+            cpu_model_="knl"
         elif [[ $model_string == *"72"* ]]; then
-            cpu_model="knm"
+            cpu_model_="knm"
         else
-            cpu_model="unknown"
-            echo "CPU model :$model_string is unknown."
-            echo "    Use default settings, which may not be the optimal one."
+            cpu_model_="unknown"
         fi
     else
         model_num=`echo $model_string | awk '{print $4}'`
         if [[ $model_num =~ ^[8|6|5|4|3]1 ]]; then
-            cpu_model="skx"
+            cpu_model_="skx"
         elif [[ $model_num =~ ^E5-[4|2|1]6|^E7-[8|4]8|^E3-12|^D[-]?15 ]]; then
-            cpu_model="bdw"
+            cpu_model_="bdw"
         else
-            cpu_model="unknown"
-            echo "CPU model :$model_string is unknown."
-            echo "    Use default settings, which may not be the optimal one."
+            cpu_model_="unknown"
         fi
     fi
+    echo ${cpu_model_}
 }
 
 function set_numa_node
 {
-    check_dependency numactl
+    # this cannot be replaced with check_dependency function,
+    # since it may be called via ssh
+    which numactl >/dev/null 2>&1
     if [ $? -ne 0 ]; then
         return
     fi
@@ -143,13 +178,12 @@ function set_numa_node
     # detect numa mode: cache and flat mode for KNL
     numa_node=($(numactl -H | grep "available" | awk -F ' ' '{print $2}'))
     if [ $numa_node -eq 1 ]; then
-        echo "    NUMA configuration: Cache mode."
         # cache mode, use numa node 0
-        numanode=0
+        numanode_=0
     else
-        echo "    NUMA configuration: Flat mode."
-        numanode=1
+        numanode_=1
     fi
+    echo ${numanode_}
 }
 
 
@@ -178,7 +212,7 @@ function execute_command
         exec_command="$xeonbin_"
     fi
 
-    if [ ${numnodes} -gt 1 ]; then
+    if [ ${numnodes} -gt 1 ] || [ $is_local_run -ne 1 ]; then
         # Produce the configuration file for mpiexec. 
         # Each line of the config file contains a # host, environment, binary name.
         cfile_=$result_dir_/nodeconfig-${cpu_model}-${numnodes}.txt
@@ -199,7 +233,7 @@ function execute_command
         $sensors_bin >$sensor_log_file
     fi
 
-    if [ ${numnodes} -eq 1 ]; then
+    if [ ${numnodes} -eq 1 ] && [ $is_local_run -eq 1 ]; then
         time GLOG_minloglevel=0 $exec_command 2>&1 | tee ${log_file}
     else
         exec_command="-l -configfile $cfile_"
@@ -367,7 +401,10 @@ function test_ssh_connection
 function get_model_fname
 {
     solver_file_=$1
-    model_file_=$(grep -w "net:" $solver_file_ | head -n 1 | awk -F ':' '{print $2}' | sed 's/\"//g' | sed 's/\r//g')
+    model_file_=$(grep -w "net:" $solver_file_ | head -n 1 | awk -F ':' '{print $2}' | sed 's/\"//g' | sed 's/\r//g' | tr -d ' ')
+    if [ "$model_file_" != "" ] && [[ "$model_file_" != /* ]]; then
+        model_file_=$root_dir/$model_file_
+    fi
     echo "$(echo $model_file_)"
 }
 
@@ -504,6 +541,18 @@ do
             cpu_model=$2
             shift
             ;;
+        --msg_priority)
+            msg_priority=$2
+            shift
+            ;;
+        --msg_priority_threshold)
+            msg_priority_threshold=$2
+            shift
+            ;;
+        --mpi_iallreduce_algo)
+            mpi_iallreduce_algo=$2
+            shift
+            ;;
         *)
             echo "Unknown option: $key"
             usage
@@ -520,9 +569,25 @@ do
     echo "    ${cpu_list[$i]}"
 done
 
+head_node=""
+if [ "$host_file" != "" ]; then
+    is_localhost_in_hostfile $host_file
+    is_local_run=$?
+    head_node=`cat $host_file | head -n 1`
+fi
+
 # if cpu model is not specified in command, detect cpu model by "lscpu" command
 if [ "$cpu_model" == "" ]; then
-    detect_cpu
+    if [ $is_local_run -eq 1 ]; then
+        cpu_model=`detect_cpu`
+    else
+        # detect cpu on remote machine
+        cpu_model=`ssh $head_node "$(typeset -f detect_cpu);detect_cpu"`
+    fi
+fi
+if [ "$cpu_model" == "unknown" ]; then
+    echo "CPU model is unknown."
+    echo "    Use default settings, which may not be the optimal one."
 fi
 
 echo ""
@@ -582,7 +647,12 @@ fi
 if [ "$model_file" != "" ]; then
     grep "backend" $model_file | grep -i "LMDB"  >/dev/null
     if [ $? -eq 0 ]; then
-        check_lmdb_files $model_file
+        if [ $is_local_run -eq 1 ]; then
+            check_lmdb_files $model_file
+        else
+            # check lmdb on remote machine
+            ssh $head_node "$(typeset -f check_lmdb_files);cd $root_dir; check_lmdb_files $model_file"
+        fi
     fi
 fi
 
@@ -597,19 +667,29 @@ fi
 
 # Names to configfile, binary (executable) files #
 # Add check for host_file's existence to support single node
-if [[ $host_file != "" ]]; then
+if [ "$host_file" != "" ]; then
     nodenames=( `cat $host_file | sort -V | uniq ` )
     if [ ${#nodenames[@]} -eq 0 ]; then
         echo "Error: empty host file! Exit."
         exit 0
     fi
     numnodes=${#nodenames[@]}
-fi
 
-# test connection between nodes via ssh
-test_ssh_connection $host_file
+    # test connection between nodes via ssh
+    test_ssh_connection $host_file
+fi
 
-set_numa_node
+if [ $is_local_run -eq 1 ]; then
+    numanode=`set_numa_node`
+else
+    # check numa config
+    numanode=`ssh $head_node "$(typeset -f set_numa_node); set_numa_node"`
+fi
+if [ $numanode -eq 0 ]; then
+    echo "    NUMA configuration: Cache mode."
+else
+    echo "    NUMA configuration: Flat mode."
+fi
 
 if [ ! -d $result_dir ]; then
     echo "Create result directory: $result_dir"
@@ -617,6 +697,7 @@ if [ ! -d $result_dir ]; then
 fi
 
 env_params="--cpu $cpu_model --debug $debug --network $network --num_mlsl_servers $num_mlsl_servers"
+env_params+=" --msg_priority $msg_priority --msg_priority_threshold $msg_priority_threshold"
 if [ "$network" == "tcp" ]; then
     env_params+=" --netmask $tcp_netmask"
 fi
@@ -627,6 +708,10 @@ if [ ${num_omp_threads} -ne 0 ]; then
     env_params+=" --num_omp_threads ${num_omp_threads}"
 fi
 
+if [ "${mpi_iallreduce_algo}" != "" ]; then
+    env_params+=" --mpi_iallreduce_algo ${mpi_iallreduce_algo}"
+fi
+
 source ${script_dir}/set_env.sh $env_params
 
 if [ "${benchmark_mode}" != "none" ]; then
diff --git a/scripts/set_env.sh b/scripts/set_env.sh
index 9d7ca6013..d38464b0a 100755
--- a/scripts/set_env.sh
+++ b/scripts/set_env.sh
@@ -27,6 +27,11 @@ numservers=0
 # pin internal threads to 2 CPU cores for reading data
 internal_thread_pin="on"
 
+msg_priority="off"
+msg_priority_threshold=""
+
+mpi_iallreduce_algo=""
+
 function init_mpi_envs
 {
     if [ ${numnodes} -eq 1 ]; then
@@ -56,13 +61,17 @@ function init_mpi_envs
 
     export I_MPI_FALLBACK=0
     export I_MPI_DEBUG=6
+
+    if [ "${mpi_iallreduce_algo}" != "" ]; then
+        export I_MPI_ADJUST_IALLREDUCE=${mpi_iallreduce_algo}
+    fi
 }
 
 
 function clear_shm
 {
     clear_command="rm -rf /dev/shm/*"
-    check_shm_command="df -h | grep shm"
+    check_shm_command="df -h /dev/shm | grep shm"
 
     # TODO: check if 40G is the minimum shm size?
     min_shm_size=40
@@ -114,7 +123,9 @@ function set_mlsl_vars
     if [ -z $MLSL_ROOT ]; then
         # use built-in mlsl if nothing is specified in ini
         mlslvars_sh=`find external/mlsl/ -name mlslvars.sh`
-        source $mlslvars_sh
+        if [ -f $mlslvars_sh ]; then
+            source $mlslvars_sh
+        fi
     fi
 
     if [ ${num_mlsl_servers} -eq -1 ]; then
@@ -131,11 +142,12 @@ function set_mlsl_vars
     export MLSL_NUM_SERVERS=${numservers}
 
     if [ ${numservers} -gt 0 ]; then
-        if [ "${cpu_model}" == "knl" ] || [ "${cpu_model}" == "knm" ]; then
-            listep=6,7,8,9
-        else
-            listep=6,7
-        fi
+        listep="6"
+        for ((i=7; i<${numservers}+6; i++))
+        do
+            listep+=",$i"
+        done
+
         export MLSL_SERVER_AFFINITY="${listep}"
         echo "MLSL_SERVER_AFFINITY: ${listep}"
     fi
@@ -144,7 +156,17 @@ function set_mlsl_vars
     if [ "$debug" == "on" ]; then
         export MLSL_LOG_LEVEL=3
     else
-        export MLSL_LOG_LEVEL=0
+        export MLSL_LOG_LEVEL=1
+    fi
+
+    if [ "$msg_priority" == "on" ]; then
+        echo "Enable priority queue."
+        export MLSL_MSG_PRIORITY=1
+
+        if [ "$msg_priority_threshold" != "" ]; then
+            echo "Priority queue threshold: $msg_priority_threshold"
+            export MLSL_MSG_PRIORITY_THRESHOLD=$msg_priority_threshold
+        fi
     fi
 }
 
@@ -243,6 +265,18 @@ do
             internal_thread_pin=$2
             shift
             ;;
+        --msg_priority)
+            msg_priority=$2
+            shift
+            ;;
+        --msg_priority_threshold)
+            msg_priority_threshold=$2
+            shift
+            ;;
+        --mpi_iallreduce_algo)
+            mpi_iallreduce_algo=$2
+            shift
+            ;;
         *)
             echo "Unknown option: $key"
             usage
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 627735ee2..ef3e36ee5 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -161,6 +161,19 @@ const Dtype* Blob<Dtype>::cpu_diff() const {
   return (const Dtype*)diff_->cpu_data();
 }
 
+#ifdef CO_SIM
+template <typename Dtype>
+Dtype* Blob<Dtype>::cpu_data_cosim() const {
+  CHECK(data_);
+  return (Dtype*)data_->cpu_data_cosim();
+}
+template <typename Dtype>
+Dtype* Blob<Dtype>::cpu_diff_cosim() const {
+  CHECK(diff_);
+   return (Dtype*)diff_->cpu_data_cosim();
+}
+#endif
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::gpu_diff() const {
   CHECK(diff_);
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
index 4c9ea32f0..3252ba983 100644
--- a/src/caffe/data_reader.cpp
+++ b/src/caffe/data_reader.cpp
@@ -177,6 +177,10 @@ DataReader::DBWrapper::DBWrapper(const LayerParameter& param) {
   cursor.reset(db->NewCursor());
 }
 
+DataReader::DBWrapper::~DBWrapper() {
+}
+
+
 DataReader::DBShuffle::DBShuffle(const LayerParameter& param):DBWrapper(param) {
   CHECK(param.data_param().backend() != DataParameter_DB_LEVELDB)
                                       << "LevelDB doesn't support shuffle";
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index e09011963..f9683215d 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1239,7 +1239,7 @@ void DataTransformer<Dtype>::RandomAlterAspectRatio(const cv::Mat& img, cv::Mat
   CHECK(max_area_ratio >= min_area_ratio);
   ResizeParameter resize_param = param_.random_aspect_ratio_param().resize_param();
   int attempt = 0;
-  while (attempt++ < 10) {
+  while (attempt++ < param_.random_aspect_ratio_param().max_attempt()) {
     float area_ratio = RandRatio(min_area_ratio, max_area_ratio, rand_num_);
     float aspect_ratio_change =
       RandRatio(min_aspect_ratio_change, 1 / min_aspect_ratio_change, rand_num_);
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index fa37e24ec..0218d9661 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -116,7 +116,7 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     }
 #endif
 #ifdef MKLDNN_SUPPORTED
-    else if (!use_dilation && ep.isEngine("MKLDNN")) {
+    else if (ep.isEngine("MKLDNN")) {
       engine = ConvolutionParameter_Engine_MKLDNN;
     }
 #endif
@@ -150,10 +150,6 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
 #endif
 #ifdef MKLDNN_SUPPORTED
   } else if (engine == ConvolutionParameter_Engine_MKLDNN) {
-    if (use_dilation) {
-      LOG(FATAL) << "MKLDNN doesn't support the dilated convolution at Layer "
-                 << param.name();
-    }
     return shared_ptr<Layer<Dtype> >(new MKLDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
diff --git a/src/caffe/layers/detection_output_layer.cpp b/src/caffe/layers/detection_output_layer.cpp
index 1237731a6..3480fd36e 100644
--- a/src/caffe/layers/detection_output_layer.cpp
+++ b/src/caffe/layers/detection_output_layer.cpp
@@ -79,14 +79,22 @@ void DetectionOutputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       detection_output_param.save_output_param();
   output_directory_ = save_output_param.output_directory();
   if (!output_directory_.empty()) {
-    if (boost::filesystem::is_directory(output_directory_)) {
+    if (boost::filesystem::is_directory(output_directory_)
+#ifdef USE_MLSL
+        && mn::is_root() == true
+#endif
+       )
       boost::filesystem::remove_all(output_directory_);
-    }
     // bug on ret val of create_dir https://svn.boost.org/trac/boost/ticket/7258
+#ifdef USE_MLSL
+    if(mn::is_root() == true){
+#endif
     boost::filesystem::create_directories(output_directory_);
-    if(!boost::filesystem::is_directory(output_directory_)) {
+    if(!boost::filesystem::is_directory(output_directory_))
       LOG(FATAL) << "Failed to create directory: " << output_directory_;
+#ifdef USE_MLSL
     }
+#endif
   }
   output_name_prefix_ = save_output_param.output_name_prefix();
   need_save_ = output_directory_ == "" ? false : true;
@@ -171,6 +179,9 @@ void DetectionOutputLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     if (name_count_ % num_test_image_ == 0) {
       // Clean all outputs.
       if (output_format_ == "VOC") {
+#ifdef USE_MLSL
+        MPI_Barrier(MPI_COMM_WORLD);
+#endif
         boost::filesystem::path output_directory(output_directory_);
         for (map<int, string>::iterator it = label_to_name_.begin();
              it != label_to_name_.end(); ++it) {
diff --git a/src/caffe/layers/mkldnn_batch_norm_layer.cpp b/src/caffe/layers/mkldnn_batch_norm_layer.cpp
index 2ef51947b..cdf73bfe5 100644
--- a/src/caffe/layers/mkldnn_batch_norm_layer.cpp
+++ b/src/caffe/layers/mkldnn_batch_norm_layer.cpp
@@ -234,9 +234,20 @@ void MKLDNNBatchNormLayer<Dtype>::InitBatchNorm(const vector<Blob<Dtype>*>& bott
     EngineParser ep(subengines);
     unsigned subEngineIndex = 0;
     BatchNormFwd_pd = NULL;
+    bool relu = this->layer_param_.batch_norm_param().relu();
+    mkldnn::primitive_attr attr;
+    mkldnn::post_ops ops;
+    if (relu) {
+        ops.append_eltwise(1.f, eltwise_relu, 0.f, 0.f);
+        attr.set_post_ops(ops);
+    }
     for(; subEngineIndex < ep.getNumberOfSubEngines(); subEngineIndex++) {
       try {
-        BatchNormFwd_pd.reset(new batch_normalization_forward::primitive_desc(BatchNormFwd_desc,
+        if (relu)
+            BatchNormFwd_pd.reset(new batch_normalization_forward::primitive_desc(BatchNormFwd_desc, attr,
+                ep.getMKLDNNSubEngine(subEngineIndex)));
+        else
+            BatchNormFwd_pd.reset(new batch_normalization_forward::primitive_desc(BatchNormFwd_desc,
                 ep.getMKLDNNSubEngine(subEngineIndex)));
       }
       catch(...) {
@@ -249,7 +260,13 @@ void MKLDNNBatchNormLayer<Dtype>::InitBatchNorm(const vector<Blob<Dtype>*>& bott
 
     // ---- Create memory  ---------------------
     if (use_weight_bias_) {
-        scaleshift_memory.reset(new memory(BatchNormFwd_pd->weights_primitive_desc(), this->scaleshift_blob_->mutable_cpu_data()));
+        //For test in train, memory address of blobs_[3] and blobs_[4] will be changed when share data from train net. If the address
+        // of blobs_[3] and blobs_[4] are continued, we will use them immediately, otherwise we will copy them to scaleshift_blob_ in Forward.
+        if((this->blobs_[3]->mutable_cpu_data() + this->blobs_[3]->offset(channels_)) == this->blobs_[4]->mutable_cpu_data()){
+            scaleshift_memory.reset(new memory(BatchNormFwd_pd->weights_primitive_desc(), this->blobs_[3]->mutable_cpu_data()));
+        }else {
+            scaleshift_memory.reset(new memory(BatchNormFwd_pd->weights_primitive_desc(), this->scaleshift_blob_->mutable_cpu_data()));
+        }
     }
 
     // ---  init primitive and prv_memory descriptors ----------------------
@@ -381,6 +398,11 @@ void MKLDNNBatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom
     // update top that head at prv
     fwd_top_data->sync_before_write();
 
+    if((this->blobs_[3]->mutable_cpu_data() + this->blobs_[3]->offset(channels_)) != this->blobs_[4]->mutable_cpu_data()){
+        caffe_copy(channels_, this->blobs_[3]->cpu_data(), this->scaleshift_blob_->mutable_cpu_data());
+        caffe_copy(channels_, this->blobs_[4]->cpu_data(), this->scaleshift_blob_->mutable_cpu_data() + scaleshift_blob_->offset(channels_));
+    }
+
     for (int stats_batch_idx = 0; stats_batch_idx < num_stats_batches_; stats_batch_idx++) {
       if (use_global_stats_) {
         // use the stored mean/variance estimates.
diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp
index d3f462998..71cef5865 100644
--- a/src/caffe/layers/mkldnn_convolution_layer.cpp
+++ b/src/caffe/layers/mkldnn_convolution_layer.cpp
@@ -79,10 +79,8 @@ template <typename Dtype>
 void MKLDNNConvolutionLayer<Dtype>::compute_output_shape()
 {
     ConvolutionLayer<Dtype>::compute_output_shape();
-    this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
-        / this->stride_h_ + 1;
-    this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
-        / this->stride_w_ + 1;
+    this->height_out_ = this->output_shape_[0];
+    this->width_out_ = this->output_shape_[1];
 }
 
 template <typename Dtype>
@@ -182,8 +180,23 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
     int32_t kw = this->kernel_w_;
     int32_t kh = this->kernel_h_;
 
-    memory::dims convolutionStrides {this->stride_h_, this->stride_w_};
-    memory::dims padding {this->pad_h_, this->pad_w_};
+    int32_t sw = this->stride_w_;
+    int32_t sh = this->stride_h_;
+
+    int32_t pw = this->pad_w_;
+    int32_t ph = this->pad_h_;
+    memory::dims convolutionStrides {sh, sw};
+    memory::dims padding {ph, pw};
+    memory::dims padding_r;
+    memory::dims dilation;
+    bool dilated_conv = false;
+    const int* dilation_data = this->dilation_.cpu_data();
+    for (int i = 0; i < this->num_spatial_axes_; ++i) {
+      dilation.push_back(dilation_data[i] - 1);
+      if (dilation_data[i] != 1) dilated_conv = true;
+    }
+    padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph);
+    padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw);
 
     // ---- Initialize memory descriptors (fromat = any) to create convolution descriptor -------------
     memory::data_type mpcsn = memory::data_type::f32;
@@ -240,19 +253,30 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
     if (this->need_quantize_) {
       if(this->scale_in_.size() > 0) this->is_float_ = true;
       int mask = 0;
-      std::vector<float> scales;
-      int count = 1; // 1 for single channel, oc for multi channel
-      for(int i=0; i<count; i++){
-          float scale;
-          if(this->is_float_){
-              scale = this->scale_out_[0] / (this->scale_in_[0] * this->scale_params_[i]);
-          } else{
-              int output_shift = this->fl_layer_out_[0] - this->fl_layer_in_[0] - this->fl_params_[i];
-              scale = pow(2. ,output_shift);
-          }
-          scales.push_back(scale);
+      int count = 1; //single channel
+      if(this->fl_params_.size() > 1 || this->scale_params_.size() > 1){
+          int oc_dim_id = 1;
+          mask = 1 << oc_dim_id;
+          count = oc;  //multi channel
       }
-      attr.set_output_scales(mask,scales);
+      std::vector<float> scales(count);
+      float scale;
+      if(this->is_float_){
+        #pragma omp parallel for if (count > 1)
+        for(int i=0; i<count; i++){
+          scale = this->scale_out_[0] / (this->scale_in_[0] * this->scale_params_[i]);
+          scales[i] = scale;
+        }
+      } else {
+        int output_shift;
+        #pragma omp parallel for if (count > 1)
+        for(int i=0; i<count; i++){
+          output_shift = this->fl_layer_out_[0] - this->fl_layer_in_[0] - this->fl_params_[i];
+          scale = pow(2. ,output_shift);
+          scales[i] = scale;
+        }
+      }
+      attr.set_output_scales(mask, scales);
       attr.set_int_output_round_mode(round_nearest);
     }
     
@@ -301,15 +325,27 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
       // ---- Initialize convolution primitive descriptor -------------
       shared_ptr<convolution_forward::desc> convFwd_desc;
       if (this->bias_term_) {
-        convFwd_desc.reset(new convolution_forward::desc(
-            propagation, convAlgorithm, init_bottom_md, init_weights_md,
-            init_bias_md, init_top_md, convolutionStrides, padding, padding,
-            padding_kind::zero));
+          if (dilated_conv)
+              convFwd_desc.reset(new convolution_forward::desc(
+                  propagation, convAlgorithm, init_bottom_md, init_weights_md,
+                  init_bias_md, init_top_md, convolutionStrides, dilation, padding, padding_r,
+                  padding_kind::zero));
+          else
+              convFwd_desc.reset(new convolution_forward::desc(
+                  propagation, convAlgorithm, init_bottom_md, init_weights_md,
+                  init_bias_md, init_top_md, convolutionStrides, padding, padding,
+                  padding_kind::zero));
       } else {
-        convFwd_desc.reset(new convolution_forward::desc(
-            propagation, convAlgorithm, init_bottom_md, init_weights_md,
-            init_top_md, convolutionStrides, padding, padding,
-            padding_kind::zero));
+          if (dilated_conv)
+              convFwd_desc.reset(new convolution_forward::desc(
+                  propagation, convAlgorithm, init_bottom_md, init_weights_md,
+                  init_top_md, convolutionStrides, dilation, padding, padding_r,
+                  padding_kind::zero));
+          else
+              convFwd_desc.reset(new convolution_forward::desc(
+                  propagation, convAlgorithm, init_bottom_md, init_weights_md,
+                  init_top_md, convolutionStrides, padding, padding,
+                  padding_kind::zero));
       }
 
       for (subEngineIndex = 0; subEngineIndex < ep.getNumberOfSubEngines();
@@ -403,53 +439,73 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
     fwd_top_data->name = "fwd_top_data      @ " + this->layer_param_.name();
     fwd_top_data_memory = fwd_top_data->create_output_memory();
 
-    if (this->need_quantize_){
-      int count = 1; // 1 for single channel, oc for multi channel
-      if(this->is_float_ || bottom_is_float){
-        std::vector<float> scale_weight;
-        for(int i=0; i<count; i++){
-          scale_weight.push_back(this->scale_params_[i]);
+    if (fwd_weights_data == NULL) {
+      if (this->need_quantize_){
+        int count = 1; //single channel
+        if(this->is_float_ || bottom_is_float){
+          if(this->scale_params_.size() > 1){
+              count = oc;  //multi channel
+          }
+          std::vector<float> scale_weight(count);
+          #pragma omp parallel for if (count > 1)
+          for(int i=0; i<count; i++){
+            scale_weight[i] = this->scale_params_[i];
+          }
+          fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, true, scale_weight));
+        } else{
+          if(this->fl_params_.size() > 1){
+              count = oc;  //multi channel
+          }
+          std::vector<int> fl_weight(count);
+          #pragma omp parallel for if (count > 1)
+          for(int i=0; i<count; i++){
+            fl_weight[i] = this->fl_params_[i];
+          }
+          fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, fl_weight));
         }
-        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, true, scale_weight));
+      } else if(bottom_is_float){
+        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, false));
       } else{
-        std::vector<int> fl_weight;
-        for(int i=0; i<count; i++){
-          fl_weight.push_back(this->fl_params_[i]);
-        }
-        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, fl_weight));
+        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this));
       }
-    } else if(bottom_is_float){
-      fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, false));
-    } else{
-      fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this));
+      fwd_weights_data->name = "fwd_weights_data  @ " + this->layer_param_.name();
+      fwd_weights_data_primitive = fwd_weights_data->create_input(true);
     }
-    fwd_weights_data->name = "fwd_weights_data  @ " + this->layer_param_.name();
-    fwd_weights_data_primitive = fwd_weights_data->create_input(true);
 
     if (this->bias_term_) {
-        shared_ptr<MemPD> prv_fwd_bias_data_memory_pd(new MemPD(convFwd_pd->bias_primitive_desc()));
-        if (this->need_quantize_){
-          int count = 1; // 1 for single channel, oc for multi channel
-          if(this->is_float_ || bottom_is_float){
-            std::vector<float> scale_bias;
-            for(int i=0; i<count; i++){
-              scale_bias.push_back(this->scale_in_[0] * this->scale_params_[i]);
+        if (fwd_bias_data == NULL) {
+          shared_ptr<MemPD> prv_fwd_bias_data_memory_pd(new MemPD(convFwd_pd->bias_primitive_desc()));
+          if (this->need_quantize_){
+            int count = 1;  //single channel
+            if(this->is_float_ || bottom_is_float){
+              if(this->scale_params_.size() > 1){
+                  count = oc;  //multi channel
+              }
+              std::vector<float> scale_bias(count);
+              #pragma omp parallel for if (count > 1)
+              for(int i=0; i<count; i++){
+                scale_bias[i] = this->scale_in_[0] * this->scale_params_[i];
+              }
+              fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, true, scale_bias));
+            } else{
+              if(this->fl_params_.size() > 1){
+                  count = oc;  //multi channel
+              }
+              std::vector<int> fl_bias(count);
+              #pragma omp parallel for if (count > 1)
+              for(int i=0; i<count; i++){
+                fl_bias[i] = this->fl_layer_in_[0] + this->fl_params_[i];
+              }
+              fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, fl_bias));
             }
-            fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, true, scale_bias));
+          } else if(bottom_is_float){
+            fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, false));
           } else{
-            std::vector<int> fl_bias;
-            for(int i=0; i<count; i++){
-              fl_bias.push_back(this->fl_layer_in_[0] + this->fl_params_[i]);
-            }
-            fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, fl_bias));
+            fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this));
           }
-        } else if(bottom_is_float){
-          fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, false));
-        } else{
-          fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this));
+          fwd_bias_data->name = "fwd_bias_data     @ " + this->layer_param_.name();
+          fwd_bias_data_primitive = fwd_bias_data->create_input(true);
         }
-        fwd_bias_data->name = "fwd_bias_data     @ " + this->layer_param_.name();
-        fwd_bias_data_primitive = fwd_bias_data->create_input(true);
         convFwd.reset(new convolution_forward(*convFwd_pd
                         , *fwd_bottom_data_primitive, *fwd_weights_data_primitive
                         , *fwd_bias_data_primitive, *fwd_top_data_memory));
@@ -519,8 +575,23 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionBwd(const vector<Blob<Dtype>*
     int32_t kw = this->kernel_w_;
     int32_t kh = this->kernel_h_;
 
-    memory::dims convolutionStrides {this->stride_h_, this->stride_w_};
-    memory::dims padding {this->pad_h_, this->pad_w_};
+    int32_t sw = this->stride_w_;
+    int32_t sh = this->stride_h_;
+
+    int32_t pw = this->pad_w_;
+    int32_t ph = this->pad_h_;
+    memory::dims convolutionStrides {sh, sw};
+    memory::dims padding {ph, pw};
+    memory::dims padding_r;
+    memory::dims dilation;
+    bool dilated_conv = false;
+    const int* dilation_data = this->dilation_.cpu_data();
+    for (int i = 0; i < this->num_spatial_axes_; ++i) {
+      dilation.push_back(dilation_data[i] - 1);
+      if (dilation_data[i] != 1) dilated_conv = true;
+    }
+    padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph);
+    padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw);
 
     // ---- Initialize memory descriptors (fromat = any) to create convolution descriptor -------------
     memory::data_type mpcsn = memory::data_type::f32;
@@ -552,26 +623,41 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionBwd(const vector<Blob<Dtype>*
         shared_ptr<convolution_backward_data::desc> convBwdData_desc;
         shared_ptr<convolution_backward_weights::desc> convBwdWeights_desc;
         if (this->bias_term_) {
-            convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
+            if (dilated_conv)
+                convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
+                            , init_bottom_md, init_weights_md, init_bias_md, init_top_md
+                            , convolutionStrides, dilation, padding, padding_r, padding_kind::zero));
+            else
+                convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
                             , init_bottom_md, init_weights_md, init_bias_md, init_top_md
                             , convolutionStrides, padding, padding, padding_kind::zero));
         } else {
-            convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
-                                                                             , init_bottom_md, init_weights_md, init_top_md
-                                                                             , convolutionStrides, padding, padding, padding_kind::zero));
+            if (dilated_conv)
+                convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
+                            , init_bottom_md, init_weights_md, init_top_md
+                            , convolutionStrides, dilation, padding, padding_r, padding_kind::zero));
+            else
+                convBwdWeights_desc.reset(new convolution_backward_weights::desc(convAlgorithm
+                            , init_bottom_md, init_weights_md, init_top_md
+                            , convolutionStrides, padding, padding, padding_kind::zero));
         }
        
-        convBwdData_desc.reset(new convolution_backward_data::desc(convAlgorithm
-                                                                   , init_bottom_md, init_weights_md, init_top_md
-                                                                   , convolutionStrides, padding, padding, padding_kind::zero));
+        if (dilated_conv)
+            convBwdData_desc.reset(new convolution_backward_data::desc(convAlgorithm
+                            , init_bottom_md, init_weights_md, init_top_md
+                            , convolutionStrides, dilation, padding, padding_r, padding_kind::zero));
+        else
+            convBwdData_desc.reset(new convolution_backward_data::desc(convAlgorithm
+                            , init_bottom_md, init_weights_md, init_top_md
+                            , convolutionStrides, padding, padding, padding_kind::zero));
 
         for(subEngineIndex=0; subEngineIndex < ep.getNumberOfSubEngines(); subEngineIndex++) {
             try {
                 convBwdData_pd.reset(new convolution_backward_data::primitive_desc(*convBwdData_desc,
-                                                                                   ep.getMKLDNNSubEngine(subEngineIndex), *convFwd_pd));
+                                          ep.getMKLDNNSubEngine(subEngineIndex), *convFwd_pd));
 
                 convBwdWeights_pd.reset(new convolution_backward_weights::primitive_desc(*convBwdWeights_desc,
-                                                                                         ep.getMKLDNNSubEngine(subEngineIndex), *convFwd_pd));
+                                          ep.getMKLDNNSubEngine(subEngineIndex), *convFwd_pd));
             }
             catch(...) {
                 continue;
diff --git a/src/caffe/layers/mkldnn_eltwise_layer.cpp b/src/caffe/layers/mkldnn_eltwise_layer.cpp
index ec4bc08f2..beb737e74 100644
--- a/src/caffe/layers/mkldnn_eltwise_layer.cpp
+++ b/src/caffe/layers/mkldnn_eltwise_layer.cpp
@@ -135,7 +135,7 @@ void MKLDNNEltwiseLayer<Dtype>::InitEltwiseFwd(const vector<Blob<Dtype>*>& botto
     int32_t ic = this->channels_;
 
     // If we just do simple adding, scale is 1.0 for all inputs we have
-    std::vector<double> scale(num_bottoms_, 1.0);
+    std::vector<float> scale(num_bottoms_, 1.0);
     //Eltwise layer is supporting multiplication coefficient and this scale value can be used for that.
     for (int i = 0; i < num_bottoms_; ++i) 
     {
diff --git a/src/caffe/layers/mkldnn_inner_product_layer.cpp b/src/caffe/layers/mkldnn_inner_product_layer.cpp
index 9994df789..b62ec3e4e 100644
--- a/src/caffe/layers/mkldnn_inner_product_layer.cpp
+++ b/src/caffe/layers/mkldnn_inner_product_layer.cpp
@@ -242,15 +242,19 @@ void MKLDNNInnerProductLayer<Dtype>::InitInnerProductFwd(const vector<Blob<Dtype
     fwd_top_data    ->name = "fwd_top_data      @ " + this->layer_param_.name();
     fwd_top_data_memory = fwd_top_data->create_output_memory();
 
-    fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this));
-    fwd_weights_data->name = "fwd_weights_data  @ " + this->layer_param_.name();
-    fwd_weights_data_primitive = fwd_weights_data->create_input(true);
+    if (fwd_weights_data == NULL) {
+      fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this));
+      fwd_weights_data->name = "fwd_weights_data  @ " + this->layer_param_.name();
+      fwd_weights_data_primitive = fwd_weights_data->create_input(true);
+    }
 
     if (this->bias_term_) {
-        shared_ptr<MemPD> prv_fwd_bias_data_memory_pd(new MemPD(ipFwd_pd->bias_primitive_desc()));
-        fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this));
-        fwd_bias_data   ->name = "fwd_bias_data     @ " + this->layer_param_.name();
-        fwd_bias_data_primitive = fwd_bias_data->create_input(true);
+        if (fwd_bias_data == NULL) {
+          shared_ptr<MemPD> prv_fwd_bias_data_memory_pd(new MemPD(ipFwd_pd->bias_primitive_desc()));
+          fwd_bias_data.reset(new MKLDNNData<Dtype>(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this));
+          fwd_bias_data   ->name = "fwd_bias_data     @ " + this->layer_param_.name();
+          fwd_bias_data_primitive = fwd_bias_data->create_input(true);
+        }
         ipFwd.reset(new inner_product_forward(*ipFwd_pd
                             , *fwd_bottom_data_primitive, *fwd_weights_data_primitive
                             , *fwd_bias_data_primitive, *fwd_top_data_memory));
diff --git a/src/caffe/layers/mkldnn_split_layer.cpp b/src/caffe/layers/mkldnn_split_layer.cpp
index c307352b0..359f52462 100644
--- a/src/caffe/layers/mkldnn_split_layer.cpp
+++ b/src/caffe/layers/mkldnn_split_layer.cpp
@@ -94,7 +94,7 @@ void MKLDNNSplitLayer<Dtype>::InitSplitBwd(const vector<Blob<Dtype>*>& bottom,
   if (std::is_same<Dtype, double>::value)  NOT_IMPLEMENTED;
 
   // We just do simple adding so scale is 1.0 for all inputs we have
-  std::vector<double> scale(top.size(), 1.0);
+  std::vector<float> scale(top.size(), 1.0);
   engine cpu_engine = CpuEngine::Instance().get_engine();
   memory::data_type data_type = memory::data_type::f32;
   // TODO: shouldn't we have format here that is well suited for earlier layer.
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index e8ce5374a..4e492b70d 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -66,17 +66,20 @@ void SoftmaxLayer<Dtype>::Forward_cpu_fast_case(
     const vector<Blob<Dtype>*>& top) {
   int channels = bottom[0]->shape(softmax_axis_);
   int dim = bottom[0]->count() / outer_num_;
-  // assert(dim == channels);
-#ifdef _OPENMP
-#pragma omp parallel for
-#endif
+
+  const Dtype* __restrict__ bottom_ = bottom[0]->cpu_data();
+  Dtype* __restrict__ top_ = top[0]->mutable_cpu_data();
+
+  #ifdef _OPENMP
+  #pragma omp parallel for
+  #endif
   for (int i = 0; i < outer_num_; ++i) {
-    const Dtype* bottom_data = bottom[0]->cpu_data() + i*dim;
-    Dtype *top_data = top[0]->mutable_cpu_data() + channels*i;
+    const Dtype* bottom_data = bottom_ + i*dim;
+    Dtype *top_data = top_ + channels*i;
 
     Dtype scale_data = bottom_data[0];
     for (int j = 1; j < channels; ++j) {
-        scale_data = std::max(scale_data, bottom_data[j]);
+      scale_data = std::max(scale_data, bottom_data[j]);
     }
 
     // subtraction
@@ -95,7 +98,10 @@ void SoftmaxLayer<Dtype>::Forward_cpu_fast_case(
     }
 
     // division
-    caffe_scal(dim, Dtype(1)/scale_data, top_data);
+    scale_data = Dtype(1.0) / scale_data;
+    for (int j = 0; j < channels; j++) {
+      top_data[j] *= scale_data;
+    }
   }
 }
 
diff --git a/src/caffe/mkl_memory.cpp b/src/caffe/mkl_memory.cpp
index d25afd24a..cd0549a02 100644
--- a/src/caffe/mkl_memory.cpp
+++ b/src/caffe/mkl_memory.cpp
@@ -68,6 +68,12 @@ void MKLMemoryDescriptorBase<Dtype>::create_conversions() {
   }
 }
 
+#ifdef CO_SIM
+template <typename Dtype>
+void MKLMemoryDescriptorBase<Dtype>::convert_from_prv_cosim(void* cpu_ptr){}
+template <typename Dtype>
+void MKLMemoryDescriptorBase<Dtype>::create_reorder_from_prv_cosim(void* cpu_ptr){}
+#endif
 template <typename Dtype>
 void MKLMemoryDescriptorBase<Dtype>::remove_conversions() {
   int status;
diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp
index d2c170ee4..6ba28da3e 100644
--- a/src/caffe/mkldnn_memory.cpp
+++ b/src/caffe/mkldnn_memory.cpp
@@ -108,20 +108,30 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
 
     primitive_attr attri;
     int mask = 0;
+    if(scale.size() > 1 || scale_ext.size() > 1){
+#ifdef DEBUG
+        LOG(INFO)<<"multi-channel";
+#endif
+        int oc_dim_id = 0;
+        mask = 1 << oc_dim_id;
+    }
 
+    int count = scale.size();
     if ( *_usr_memory_pd != *_prv_memory_pd) {
-        std::vector<float> scales_u2p;
-        for(int i=0; i<scale.size(); i++){
-            scales_u2p.push_back(scale[i]);
+        std::vector<float> scales_u2p(count);
+        #pragma omp parallel for if (count > 1)
+        for(int i=0; i < count; i++){
+            scales_u2p[i] = scale[i];
         }
         attri.set_output_scales(mask, scales_u2p);
         attri.set_int_output_round_mode(round_nearest);
         _reorder_usr2prv_pd = shared_ptr<reorder::primitive_desc>(
                 new reorder::primitive_desc(*_usr_memory_pd, *_prv_memory_pd, attri));
 
-        std::vector<float> scales_p2u;
-        for(int i=0; i<scale.size(); i++){
-            scales_p2u.push_back(1. / scale[i]);
+        std::vector<float> scales_p2u(count);
+        #pragma omp parallel for if (count > 1)
+        for(int i=0; i < count; i++){
+            scales_p2u[i] = (1. / scale[i]);
         }
         attri.set_output_scales(mask, scales_p2u); 
         attri.set_int_output_round_mode(round_nearest);
@@ -135,10 +145,12 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
 #endif
             _reorder_extprv2prv_pd = NULL;
         }else{
-            std::vector<float> scales_e2p;
-            for(int i=0; i<scale.size(); i++){
-                float shift_scale = scale[i] / scale_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_scale() will always be 0 ?
-                scales_e2p.push_back(shift_scale);
+            std::vector<float> scales_e2p(count);
+            float shift_scale;
+            #pragma omp parallel for if (count > 1)
+            for(int i=0; i < count; i++){
+                shift_scale = scale[i] / scale_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_scale() will always be 0 ?
+                scales_e2p[i] = shift_scale;
             }
             attri.set_output_scales(mask, scales_e2p);
             attri.set_int_output_round_mode(round_nearest);
@@ -156,22 +168,33 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<i
 
     primitive_attr attri;
     int mask = 0;
+    if(fl.size() > 1 || fl_ext.size() > 1){
+#ifdef DEBUG
+        LOG(INFO)<<"multi-channel";
+#endif
+        int oc_dim_id = 0;
+        mask = 1 << oc_dim_id;
+    }
 
+    int count = fl.size();
+    float scale;
     if ( *_usr_memory_pd != *_prv_memory_pd) {
-        std::vector<float> scales_u2p;
-        for(int i=0; i<fl.size(); i++){
-            float scale = pow(2, fl[i]);
-            scales_u2p.push_back(scale);
+        std::vector<float> scales_u2p(count);
+        #pragma omp parallel for if (count > 1)
+        for(int i = 0; i < count; i++){
+            scale = pow(2, fl[i]);
+            scales_u2p[i] = scale;
         }
         attri.set_output_scales(mask, scales_u2p);
         attri.set_int_output_round_mode(round_nearest);
         _reorder_usr2prv_pd = shared_ptr<reorder::primitive_desc>(
                 new reorder::primitive_desc(*_usr_memory_pd, *_prv_memory_pd, attri));
 
-        std::vector<float> scales_p2u;
-        for(int i=0; i<fl.size(); i++){
-            float scale = pow(2, -fl[i]);
-            scales_p2u.push_back(scale);
+        std::vector<float> scales_p2u(count);
+        #pragma omp parallel for if (count > 1)
+        for(int i = 0; i < count; i++){
+          scale = pow(2, -fl[i]);
+          scales_p2u[i] = scale;
         }
         attri.set_output_scales(mask, scales_p2u);
         attri.set_int_output_round_mode(round_nearest);
@@ -185,11 +208,13 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<i
 #endif
             _reorder_extprv2prv_pd = NULL;
         }else{
-            std::vector<float> scales_e2p;
-            for(int i=0; i<fl.size(); i++){
-                int shift_fl = fl[i] - fl_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_fl() will always be 0 ?
-                float scale = pow(2, shift_fl);
-                scales_e2p.push_back(scale);
+            std::vector<float> scales_e2p(count);
+            int shift_fl;
+            #pragma omp parallel for if (count > 1)
+            for(int i = 0; i < count; i++){
+                shift_fl = fl[i] - fl_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_fl() will always be 0 ?
+                scale = pow(2, shift_fl);
+                scales_e2p[i] = scale;
             }
             attri.set_output_scales(mask, scales_e2p);
             attri.set_int_output_round_mode(round_nearest);
@@ -283,7 +308,46 @@ void MKLDNNMemoryDescriptor<Dtype, is_diff>::convert_to_prv(void* cpu_ptr)
     this->_reorder_usr2prv.submit();
     PERFORMANCE_MEASUREMENT_END_STATIC("mkldnn_conversion");
 }
+#ifdef CO_SIM
+template <typename Dtype, bool is_diff>
+void MKLDNNMemoryDescriptor<Dtype, is_diff>::convert_from_prv_cosim(void* cpu_ptr_cosim)
+{
+    if(this->_reorder_prv2usr_pd == NULL)
+        return;
+    create_reorder_from_prv_cosim(cpu_ptr_cosim);
+    this->_reorder_prv2usr_cosim.submit();
+
+}
 
+template <typename Dtype, bool is_diff>
+void MKLDNNMemoryDescriptor<Dtype, is_diff>::create_reorder_from_prv_cosim(void* cpu_ptr_cosim)
+{
+    CHECK(cpu_ptr_cosim);
+    CHECK(this->_usr_memory_pd);
+    CHECK(this->_prv_memory_pd);
+    CHECK(this->_reorder_prv2usr_pd);
+
+    this->_usr_memory_cosim = this->_usr_memory;
+    this->_reorder_prv2usr_cosim.aprimitive = this->_reorder_prv2usr.aprimitive;
+
+    // Used to save data copy from prv.
+    this->_prv_memory_cosim.reset(new memory(*this->_prv_memory_pd));
+
+    // Copy prv data to _prv_memory_cosim.
+    memcpy(this->_prv_memory_cosim->get_data_handle(), this->get_prv_ptr(), this->prv_size());
+
+    // Wrap _prv_memory_cosim.
+    this->at_prv_cosim.reset(new primitive::at(*this->_prv_memory_cosim));
+
+    if(this->_usr_memory == NULL || this->_cpu_ptr != cpu_ptr_cosim)
+        this->_usr_memory_cosim.reset(new memory(*this->_usr_memory_pd, cpu_ptr_cosim));
+    if(this->_reorder_prv2usr.aprimitive == NULL || this->_cpu_ptr != cpu_ptr_cosim){
+
+        // Create primitive for reorder.
+        this->_reorder_prv2usr_cosim.aprimitive.reset(new reorder(*this->_reorder_prv2usr_pd, *this->at_prv_cosim, *this->_usr_memory_cosim));
+    }
+}
+#endif
 template <typename Dtype, bool is_diff>
 void MKLDNNMemoryDescriptor<Dtype, is_diff>::create_reorder_from_prv(void* cpu_ptr)
 {
@@ -488,7 +552,7 @@ void MKLDNNMemoryDescriptor<Dtype, is_diff>::sync_before_read()
         this->convert_to_prv(const_cast<Dtype*>(is_diff ? this->_blob->cpu_diff() : this->_blob->cpu_data()));
         // if blob has not prv descriptor then set it to avoid conversions on next iterations
         if (is_diff) {
-            this->_blob->set_prv_diff_descriptor(this->get_shared_ptr(), true);
+            this->_blob->set_prv_diff_descriptor(this->get_shared_ptr(), false);
             // Original:
             // below line designated to set correspondent SyncedMemory->_head to HEAD_AT_CPU
             // TODO: need to optimize
diff --git a/src/caffe/multinode/apply_mn_param.cpp b/src/caffe/multinode/apply_mn_param.cpp
index 1051c0913..807c4bb59 100644
--- a/src/caffe/multinode/apply_mn_param.cpp
+++ b/src/caffe/multinode/apply_mn_param.cpp
@@ -57,6 +57,7 @@ void ApplyMultinodeParams(const NetParameter& param,
   // aux map for inserting MnActivationLayer
   map<string, MnActivationParameter> blob_param_map;
   MultinodeParameter mn_param = param.multinode();
+  const MnParamGradCompressLayerTypeList &compress_layer_list = mn_param.compress_layer_type_list();
 
   // Step 1: Identify all the layers having global net params
   for (int param_id = 0; param_id < mn_param.model_parallel_size(); param_id++) {
@@ -255,6 +256,24 @@ void ApplyMultinodeParams(const NetParameter& param,
       }
     }
   }
+
+    // Step 4: Set MnParamGradCompressParameter on each layer param if the
+  //         layer type is in the compress_layer_type list.
+  for (int i = 0; i < param_with_mn->layer_size(); i++) {
+    LayerParameter *layer_param = param_with_mn->mutable_layer(i);
+    string layer_type = layer_param->type();
+    for (int j = 0; j < compress_layer_list.layer_type_size(); j++) {
+      if (compress_layer_list.layer_type(j) == layer_type) {
+        MnParamGradCompressParameter *grad_comp_param = layer_param->mutable_mn_grad_compress_param();
+        // If param_grad_compress_enable is not set, we set it with default value.
+        if (grad_comp_param->param_grad_compress_enable_size() == 0) {
+          // In default, we only compress first blob (weight)'s grad.
+          grad_comp_param->add_param_grad_compress_enable(true);
+        }
+        break;
+      }
+    }
+  }
 }
 
 template <typename Dtype>
diff --git a/src/caffe/multinode/async_param_server.cpp b/src/caffe/multinode/async_param_server.cpp
index a403bafb5..36ae76099 100644
--- a/src/caffe/multinode/async_param_server.cpp
+++ b/src/caffe/multinode/async_param_server.cpp
@@ -50,7 +50,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 namespace caffe {
   namespace mn {
-
     using std::make_pair;
 
     template <typename Dtype>
@@ -63,16 +62,16 @@ namespace caffe {
       int mpi_rank = get_node_rank();
       shared_ptr<Net<Dtype>> net = solver_->net();
       const vector<Blob<Dtype> *> &net_params = net->learnable_params();
-      
+      const std::vector<bool>& layer_need_backward{ net->layer_need_backward() };
+     
       for (int i = 0; i < get_num_groups(); i++) {
         int root_rank = get_group_root_rank(i);
         //iterate over layers and skip the ones without params
         for (int j = 0; j < net->layers().size(); j++) {
           shared_ptr<Layer<Dtype>> layer = net->layers()[j];
-          //skip layers w/o parameters
-          if ((layer->layerOp == nullptr) || !(layer->layerOp->HasParameterSets())) {
-            continue;
-          }
+          if (!layer_need_backward[j])
+              continue;
+
           const MultinodeLayerParameter & mn_layer_param = layer->layer_param().multinode();
           int model_parts = mn_layer_param.model_parts();
           int mn_num_nodes = mn_layer_param.num_nodes();
@@ -105,7 +104,8 @@ namespace caffe {
           }
         }
       }
-      total_update_ = total_send_ = recv_tasks_.size() * (solver_->param().max_iter() - 1);
+      // assumed iter is started from 0
+      total_update_ = total_send_ = recv_tasks_.size() * solver_->param().max_iter();
     }
 
     template <typename Dtype>
@@ -157,14 +157,18 @@ namespace caffe {
         // apply update
         int blob_wise_iter = async_iter_[make_pair(task.param_id_, task.part_id_) ];
         solver_->set_iter(blob_wise_iter);
+
         // TODO: supports partial param update per model parts
         solver_->ApplyUpdate(task.param_id_);
 
+        DLOG(INFO) << "PS (iter " << blob_wise_iter << "): param id=" << task.param_id_ << " weight=" << net_params[task.param_id_]->sumsq_diff();
+        DLOG(INFO) << "PS (iter " << blob_wise_iter << "): param id=" << task.param_id_ << " data=" << net_params[task.param_id_]->sumsq_data();
+        
         //clean up
         solver_->net()->ClearParamDiffs(task.param_id_);
         async_iter_[ make_pair(task.param_id_, task.part_id_) ] += 1;
         update_cnt_ += 1;
-        
+
         // copy model(data) in solver to mpi buffer
         mpi_buf = send_buf_[make_pair(root_rank, task.param_id_)].first;
         caffe_copy(count / task.num_parts_,
diff --git a/src/caffe/multinode/mlsl.cpp b/src/caffe/multinode/mlsl.cpp
index 90b6456a6..69b3f0a66 100644
--- a/src/caffe/multinode/mlsl.cpp
+++ b/src/caffe/multinode/mlsl.cpp
@@ -59,16 +59,17 @@ namespace caffe {
           distrib_map =
             new std::map<std::pair<int,int>, boost::shared_ptr<Distribution>>();
           if (use_param_server()) {
+            std::string config_str;
             if (is_param_server()) {
-              // this is for paramter servers
-              MLSL::Environment::GetEnv().Configure("color=0");
+              // this is for parameter servers
+              config_str = "color=0";
             }
             else {
               // this is for workers
               int group_id = get_group_id();
-              std::string config_str = "color=" + std::to_string(group_id + 1);
-              MLSL::Environment::GetEnv().Configure(config_str.c_str());
+              config_str = "color=" + std::to_string(group_id + 1);
             }
+            MLSL::Environment::GetEnv().Configure(config_str.c_str());
           }
 #ifdef ENABLE_WEIGHT_GRAD_COMPRESSION
           get_weight_grad_compress_info();
diff --git a/src/caffe/multinode/multi_solver.cpp b/src/caffe/multinode/multi_solver.cpp
index 686e076eb..96705d9e2 100644
--- a/src/caffe/multinode/multi_solver.cpp
+++ b/src/caffe/multinode/multi_solver.cpp
@@ -108,7 +108,7 @@ inline bool MultiSolver<Dtype>::IsSkipSyncGradient(int layer_id) {
 
   if (!layer_need_backward[layer_id] || ((layers[layer_id]->layerOp != nullptr)
         && !layers[layer_id]->layerOp->HasParameterSets())) {
-    DLOG(INFO) << "No need for synchronizing gradients for layer # " << layer_id;
+    DLOG(INFO) << "No need for synchronizing gradients for layer #" << layer_id;
     return true;
   }
   return false;
@@ -130,81 +130,31 @@ inline bool MultiSolver<Dtype>::WaitGradient(int layer_id) {
 template <typename Dtype>
 inline void MultiSolver<Dtype>::UpdateGradient(int layer_id) {
 #ifdef FW_OVERLAP_OPT
-    CHECK(layer_finished_flags_[layer_id]);
+  CHECK(layer_finished_flags_[layer_id]);
 #endif
-    PERFORMANCE_MEASUREMENT_BEGIN();
-    for (int j = 0; j < callbacks_.size(); ++j) {
-      callbacks_[j]->apply_updates(layer_id);
-    }
-    PERFORMANCE_MEASUREMENT_END_STATIC("weights_update");
+  PERFORMANCE_MEASUREMENT_BEGIN();
+  for (int j = 0; j < callbacks_.size(); ++j) {
+    callbacks_[j]->apply_updates(layer_id);
+  }
+  PERFORMANCE_MEASUREMENT_END_STATIC("weights_update");
 }
 
 template <typename Dtype>
-Dtype MultiSolver<Dtype>::ForwardBackwardImpl(bool first, bool last) {
-  Dtype loss = 0;
+inline Dtype MultiSolver<Dtype>::ForwardFromTo(int start, int end) {
   Net<Dtype>& net = *root_solver_->net();
-  const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
-  const std::vector<bool>& layer_need_backward{ net.layer_need_backward() };
-
-  for (int i = 0; i < layers.size(); ++i) {
-#ifdef FW_OVERLAP_OPT
-    if (first) {
-      LAYER_WAIT_TIMING_START(i);
-      while (layer_finished_flags_[i] == false) {
-        if (IsSkipSyncGradient(i))
-          break;
-        if (WaitGradient(i)) {
-          // The function call cannot be moved out of while loop. Otherwise,
-          // at first iteration, additional UpdateGradient will be called,
-          // even if no gradient is synced.
-          LAYER_TIMING_START(first_update, i);
-          UpdateGradient(i);
-          LAYER_TIMING_STOP_2(update, first_update, i);
-
-          // The update time for layer i must be removed from waitcomm time
-          // for layer i
-          LAYER_REMOVE_UPDATE_TIME(i, i);
-          break;
-        }
-
-        // wait and update gradient for next layers
-        for (int k=i+1; k<layers.size(); k++) {
-          if (layer_finished_flags_[k] || IsSkipSyncGradient(k)) {
-            layer_finished_flags_[k] = true;
-            continue;
-          }
-          if (WaitGradient(k)) {
-            LAYER_TIMING_START(first_update, k);
-            UpdateGradient(k);
-            LAYER_TIMING_STOP_2(update, first_update, k);
-
-            // The update time for layer k must be removed from waitcomm time
-            // for layer i
-            LAYER_REMOVE_UPDATE_TIME(i, k);
-            break;
-          }
-        }
-      }
-      LAYER_WAIT_TIMING_STOP(i);
-      // set flag to false after updating gradient
-      layer_finished_flags_[i] = false;
-    }
-#endif
+  return net.ForwardFromTo(start, end);
+}
 
-    loss += net.ForwardFromTo(i, i);
-  }
-  
-  // Clear parameter diffs after communication is finished (that is, after 
-  // calling WaitGradientComm)
-  if (first) {
-    root_solver_->net()->ClearParamDiffs();
-  }
+template <typename Dtype>
+inline void MultiSolver<Dtype>::BackwardFromTo(int start, int end, bool last) {
+  Net<Dtype>& net = *root_solver_->net();
+  const std::vector<bool>& layer_need_backward{ net.layer_need_backward() };
 
-  for (int i = layers.size() - 1; i >= 0; --i) {
+  for (int i=start; i>=end; --i) {
     if (!layer_need_backward[i]) {
       continue;
     }
-    
+
     net.BackwardFromTo(i, i);
 
     LAYER_TIMING_START(startcomm, i);
@@ -215,39 +165,161 @@ Dtype MultiSolver<Dtype>::ForwardBackwardImpl(bool first, bool last) {
     }
     LAYER_TIMING_STOP(startcomm, i);
   }
+}
 
+template <typename Dtype>
+inline void MultiSolver<Dtype>::Backward(bool last) {
+  Net<Dtype>& net = *root_solver_->net();
+  const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
+
+  for (int i = layers.size() - 1; i >= 0; --i) {
 #ifdef FW_OVERLAP_OPT
-  int iter = root_solver_->iter();
-  int max_iter = root_solver_->param().max_iter();
-  bool test = (root_solver_->param().test_interval()
-      && ((iter + 1) % root_solver_->param().test_interval() == 0));
-  bool last_iter_wait_flag = last && (test || (iter == max_iter - 1));
-#else
-  bool last_iter_wait_flag = last;
+    if (layer_finished_flags_[i])
+      layer_finished_flags_[i] = false;
 #endif
 
-  if (last_iter_wait_flag) {
-    for (int i = 0; i < layers.size(); ++i) {
-      LAYER_TIMING_START(waitcomm, i);
-      if (IsSkipSyncGradient(i)) {
-        LAYER_TIMING_STOP(waitcomm, i);
+    BackwardFromTo(i, i, last);
+  }
+}
+
+#ifdef FW_OVERLAP_OPT
+template <typename Dtype>
+void MultiSolver<Dtype>::WaitAndUpdateBeforeFwd(int layer_id) {
+  LAYER_WAIT_TIMING_START(layer_id);
+  while (layer_finished_flags_[layer_id] == false) {
+    if (IsSkipSyncGradient(layer_id))
+      break;
+    if (WaitGradient(layer_id)) {
+      // The function call cannot be moved out of while loop. Otherwise,
+      // at first iteration, additional UpdateGradient will be called,
+      // even if no gradient is synced.
+      LAYER_TIMING_START(first_update, layer_id);
+      UpdateGradient(layer_id);
+      LAYER_TIMING_STOP_2(update, first_update, layer_id);
+
+      // The update time for layer i must be removed from waitcomm time
+      // for layer i
+      LAYER_REMOVE_UPDATE_TIME(layer_id, layer_id);
+      break;
+    }
+
+    Net<Dtype>& net = *root_solver_->net();
+    const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
+
+    // wait and update gradient for next layers
+    for (int k=layer_id+1; k<layers.size(); k++) {
+      if (layer_finished_flags_[k] || IsSkipSyncGradient(k)) {
+        layer_finished_flags_[k] = true;
         continue;
       }
+      if (WaitGradient(k)) {
+        LAYER_TIMING_START(first_update, k);
+        UpdateGradient(k);
+        LAYER_TIMING_STOP_2(update, first_update, k);
+
+        // The update time for layer k must be removed from waitcomm time
+        // for layer i
+        LAYER_REMOVE_UPDATE_TIME(layer_id, k);
+        break;
+      }
+    }
+  }
+  LAYER_WAIT_TIMING_STOP(layer_id);
+  // set flag to false after updating gradient
+  layer_finished_flags_[layer_id] = false;
+}
+
+template <typename Dtype>
+inline Dtype MultiSolver<Dtype>::UpdateAndForward(bool first) {
+  Dtype loss = 0;
+  Net<Dtype>& net = *root_solver_->net();
+  const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
+
+  for (int i = 0; i < layers.size(); ++i) {
+    if (first) {
+      WaitAndUpdateBeforeFwd(i);
+    }
+    loss += ForwardFromTo(i, i);
+  }
+  return loss;
+}
+#endif
+
+template <typename Dtype>
+inline Dtype MultiSolver<Dtype>::Forward() {
+  Dtype loss = 0;
+  Net<Dtype>& net = *root_solver_->net();
+  const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
+
+  for (int i = 0; i < layers.size(); ++i) {
+    loss += ForwardFromTo(i, i);
+  }
+  return loss;
+}
+
+template <typename Dtype>
+inline void MultiSolver<Dtype>::WaitAndUpdate() {
+  Net<Dtype>& net = *root_solver_->net();
+  const std::vector<shared_ptr<Layer<Dtype>>>& layers{ net.layers() };
+
+  for (int i = 0; i < layers.size(); ++i) {
+    LAYER_TIMING_START(waitcomm, i);
+    if (IsSkipSyncGradient(i)) {
+      LAYER_TIMING_STOP(waitcomm, i);
+      continue;
+    }
 
 #ifdef FW_OVERLAP_OPT
-      while (
+    while (
 #endif
         WaitGradient(i)
 #ifdef FW_OVERLAP_OPT
-          == false)
+      == false)
 #endif
-      ;
-      LAYER_TIMING_STOP(waitcomm, i);
+        ;
+    LAYER_TIMING_STOP(waitcomm, i);
 
-      LAYER_TIMING_START(update, i);
-      UpdateGradient(i);
-      LAYER_TIMING_STOP(update, i);
-    }
+    LAYER_TIMING_START(update, i);
+    UpdateGradient(i);
+    LAYER_TIMING_STOP(update, i);
+  }
+}
+
+template <typename Dtype>
+inline void MultiSolver<Dtype>::ClearParamDiffs() {
+  root_solver_->net()->ClearParamDiffs();
+}
+
+template <typename Dtype>
+Dtype MultiSolver<Dtype>::ForwardBackwardImpl(bool first, bool last) {
+  Dtype loss = 0;
+
+#ifdef FW_OVERLAP_OPT
+  loss = UpdateAndForward(first);
+#else
+  loss = Forward();
+#endif
+
+  // Clear parameter diffs after communication is finished (that is, after 
+  // calling WaitGradientComm)
+  if (first) {
+    ClearParamDiffs();
+  }
+
+  Backward(last);
+
+#ifdef FW_OVERLAP_OPT
+  int iter = root_solver_->iter();
+  int max_iter = root_solver_->param().max_iter();
+  bool test = (root_solver_->param().test_interval()
+      && ((iter + 1) % root_solver_->param().test_interval() == 0));
+  bool last_iter_wait_flag = last && (test || (iter == max_iter - 1));
+#else
+  bool last_iter_wait_flag = last;
+#endif
+
+  if (last_iter_wait_flag) {
+    WaitAndUpdate();
   }
 
   DLOG(WARNING) << "iter " << root_solver_->iter() << ", loss " << loss;
diff --git a/src/caffe/multinode/multi_sync.cpp b/src/caffe/multinode/multi_sync.cpp
index 217f29e2a..0f8f56811 100644
--- a/src/caffe/multinode/multi_sync.cpp
+++ b/src/caffe/multinode/multi_sync.cpp
@@ -50,8 +50,8 @@ MultiSync<Dtype>::MultiSync(shared_ptr<Solver<Dtype> > root_solver)
           reduce_req_vec(net_params.size(), NULL),
           irecv_req_vec(net_params.size(), MPI_REQUEST_NULL),
           broadcast_req_vec(net_params.size(), NULL),
-          irecv_done(net_params.size(), true),
-          broadcast_launched(net_params.size(), true),
+          irecv_done(net_params.size(), false),
+          broadcast_launched(net_params.size(), false),
           distrib_bcast(NULL) {
   root_solver->param().set_disabled_update(true);
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 646b66b8d..89a741bd2 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -107,7 +107,6 @@ namespace caffe {
 
 #endif /* CAFFE_PER_LAYER_TIMINGS */
 
-
 template <typename Dtype>
 Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
     : root_net_(root_net) {
@@ -211,9 +210,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     fflush(0);
   }
 
-#ifdef USE_MLSL
-  int global_batch_size = -1;
-#endif
   // Basically, build all the layers and set up their connections.
   name_ = param.name();
   map<string, int> blob_name_to_idx;
@@ -300,46 +296,20 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
 
 #ifdef USE_MLSL
-    if (!layer_param.type().compare("Data")       ||
-        !layer_param.type().compare("DummyData")  ||
-        !layer_param.type().compare("ImageData")  ||
-        !layer_param.type().compare("HDF5Data")   ||
-        !layer_param.type().compare("MemoryData") ||
-        !layer_param.type().compare("Input") ||
-        !layer_param.type().compare("WindowData") ||
-        !layer_param.type().compare("AnnotatedData")) {
-
-        // FIXME: retrieve batch_size from top[0]->shape[0] when MLSL stuff will be moved from LayerSetUp
-        //int batch_size = top_vecs_[layer_id][0]->shape(0);
-
-        int batch_size = 0;
-        if (!layer_param.type().compare("Data"))
-            batch_size = layer_param.data_param().batch_size();
-        else if (!layer_param.type().compare("DummyData"))
-            batch_size = layer_param.dummy_data_param().shape(0).dim(0);
-        else if (!layer_param.type().compare("ImageData"))
-            batch_size = layer_param.image_data_param().batch_size();
-        else if (!layer_param.type().compare("HDF5Data"))
-            batch_size = layer_param.hdf5_data_param().batch_size();
-        else if (!layer_param.type().compare("MemoryData"))
-            batch_size = layer_param.memory_data_param().batch_size();
-        else if (!layer_param.type().compare("WindowData"))
-            batch_size = layer_param.window_data_param().batch_size();
-        else if (!layer_param.type().compare("AnnotatedData"))
-            batch_size = layer_param.data_param().batch_size();
-        else if (!layer_param.type().compare("Input")
-            && layer_param.input_param().shape(0).dim().size())
-            batch_size = layer_param.input_param().shape(0).dim(0);
-
-        if (caffe::TRAIN == param.state().phase()) {
-            LOG(WARNING) << "SetMinibatchSize " << batch_size;
-            if (global_batch_size < 0) {
-              global_batch_size = batch_size * mn::get_group_size();
-              mn::train::set_global_minibatch_size(global_batch_size);
-            } else {
-              CHECK_EQ(global_batch_size, batch_size * mn::get_group_size());
-            }
-        }
+    if (caffe::TRAIN == param.state().phase()) {
+      // global batch size must be a multiple of data_parts
+      int global_batch_size = mn::train::get_global_minibatch_size();
+      int fake_batch_size = mn::get_distrib()->get_data_parts();
+      if (mn::use_param_server() && mn::is_param_server()) {
+        fake_batch_size = mn::nServer;
+      }
+
+      if (global_batch_size == 0) {
+        LOG(WARNING) << "SetMinibatchSize " << fake_batch_size;
+        mn::train::set_global_minibatch_size(fake_batch_size);
+      } else {
+        CHECK_EQ(global_batch_size, fake_batch_size);
+      }
     }
 #endif /* USE_MLSL */
 
@@ -555,42 +525,55 @@ template <typename Dtype>
 void Net<Dtype>::CompileNet(const NetParameter& param,
     NetParameter* param_compiled) {
 
-  NetParameter param_temp0;
-#ifndef DISABLE_BN_FOLDING
-  param_temp0.CopyFrom(param);
-  param_temp0.clear_layer();
-  RemoveBNScale<Dtype>(param, &param_temp0);
-#else
-  param_temp0 = param;
+  #define NUM_OF_RULES sizeof(CompileRules)/sizeof(CompileRules[0])
+  #define COMPILE_BN_FOLDING_INDEX 0
+  #define COMPILE_CONV_RELU_FUSION_INDEX 2
+  #define COMPILE_BN_RELU_FUSION_INDEX 3
+  #define COMPILE_SPARSE_INDEX 5
+  #define COMPILE_CONV_SUM_FUSION_INDEX 6
+  int i, current = 0;
+  NetParameter param_temp[2];
+  void (*CompileRules[]) (const NetParameter& param, NetParameter* param_compiled) =
+    {RemoveBNScale<Dtype>, CompilationRuleRemoveScale, CompilationRuleConvReluFusion,
+    CompilationRuleFuseBnRelu, CompilationRuleBNInplace, CompilationRuleSparse, CompilationRuleConvSumFusion};
+
+  bool disabled[NUM_OF_RULES] = {false};
+
+#ifdef DISABLE_BN_FOLDING
+  disabled[COMPILE_BN_FOLDING_INDEX] = true;
+#endif
+#ifdef DISABLE_CONV_RELU_FUSION
+  disabled[COMPILE_CONV_RELU_FUSION_INDEX] = true;
+#endif
+#ifdef DISABLE_BN_RELU_FUSION
+  disabled[COMPILE_BN_RELU_FUSION_INDEX] = true;
 #endif
-  NetParameter param_temp;  // temporary compiled param
-  param_temp.CopyFrom(param_temp0);
-  param_temp.clear_layer();    // Remove layers
-  CompilationRuleOne(param_temp0, &param_temp);
-
-  NetParameter param_temp2;  // temporary compiled param
-  param_temp2.CopyFrom(param_temp);
-  param_temp2.clear_layer();   // Remove layers
-  CompilationRuleTwo(param_temp, &param_temp2);
-
 #ifdef DISABLE_CONV_SUM_FUSION
-  param_compiled->CopyFrom(param_temp2);
-  param_compiled->clear_layer();    // Remove layers
-  CompilationRuleThree(param_temp2, param_compiled);
-#else
-  NetParameter param_temp3;
-  param_temp3.CopyFrom(param_temp2);
-  param_temp3.clear_layer();
-  CompilationRuleThree(param_temp2, &param_temp3);
-
-  param_compiled->CopyFrom(param_temp3);
-  param_compiled->clear_layer();
-  CompilationRuleFour(param_temp3, param_compiled);
-#endif 
+  disabled[COMPILE_CONV_SUM_FUSION_INDEX] = true;
+#endif
+#ifdef DISABLE_SPARSE
+  disabled[COMPILE_SPARSE_INDEX] = true;
+#endif
+
+  param_temp[current].CopyFrom(param);
+  for (i = 0; i < NUM_OF_RULES; i++)
+    if (!disabled[i]) {
+      param_temp[1 - current].CopyFrom(param_temp[current]);
+      param_temp[1 - current].clear_layer();   // Remove layers
+      (*CompileRules[i]) (param_temp[current], &param_temp[1 - current]);
+      current = 1 - current;
+    }
+  param_compiled->CopyFrom(param_temp[current]);
+  #undef NUM_OF_RULES
+  #undef COMPILE_BN_FOLDING_INDEX
+  #undef COMPILE_CONV_RELU_FUSION_INDEX
+  #undef COMPILE_BN_RELU_FUSION_INDEX
+  #undef DISABLE_SPARSE_INDEX
+  #undef COMPILE_CONV_SUM_FUSION_INDEX
 }
 
 template <typename Dtype>
-void Net<Dtype>::CompilationRuleOne(const NetParameter& param,
+void Net<Dtype>::CompilationRuleRemoveScale(const NetParameter& param,
                                     NetParameter* param_compiled) {
 
   bool merge_bn_scale = false;
@@ -680,7 +663,7 @@ void Net<Dtype>::CompilationRuleOne(const NetParameter& param,
 
 
 template <typename Dtype>
-void Net<Dtype>::CompilationRuleTwo(const NetParameter& param,
+void Net<Dtype>::CompilationRuleConvReluFusion(const NetParameter& param,
                                     NetParameter* param_compiled) {
   std::set<std::string> layers_to_drop;
   bool use_negative_slope = false;
@@ -705,16 +688,6 @@ void Net<Dtype>::CompilationRuleTwo(const NetParameter& param,
           (layer_param->engine() == "") &&
           (param.engine().compare(0, 6, "MKLDNN") == 0 &&
            param.engine().find(":DLA", 6) == string::npos)))) {
-      // check if Dialation is larger than 1. if yes, don't fuse the following Relu layer with this conv layer
-      // as MKLDNN doesn't support dilation convolution yet.
-      bool dilation = false;
-      for (int i = 0; i < layer_param->convolution_param().dilation_size(); ++i) {
-        if (layer_param->convolution_param().dilation(i) > 1) {
-          dilation = true;
-          break;
-        }
-      }
-
       std::vector<const LayerParameter*> consumer_layer_params;
       GetBlobConsumers(consumer_layer_params, layer_param->top(0),
                        param, i+1 < param.layer_size() ? i+1 : i);
@@ -724,8 +697,7 @@ void Net<Dtype>::CompilationRuleTwo(const NetParameter& param,
 
       // Consumer layer of blob produced by Conv
       // has to be ReLU layer with one Input Blob
-      if (!dilation &&
-          (consumer_layer_param.type().compare("ReLU") == 0) &&
+      if ((consumer_layer_param.type().compare("ReLU") == 0) &&
           ((consumer_layer_param.relu_param().engine() == ReLUParameter_Engine_MKLDNN) ||
            ((consumer_layer_param.relu_param().engine() == ReLUParameter_Engine_DEFAULT) &&
             (consumer_layer_param.engine().compare(0, 6, "MKLDNN") == 0 &&
@@ -783,7 +755,7 @@ void Net<Dtype>::CompilationRuleTwo(const NetParameter& param,
 }
 
 template <typename Dtype>
-void Net<Dtype>::CompilationRuleThree(const NetParameter& param,
+void Net<Dtype>::CompilationRuleBNInplace(const NetParameter& param,
                                       NetParameter* param_compiled) {
   for (int i = 0; i < param.layer_size(); ++i) {
     LayerParameter* layer_param =
@@ -889,7 +861,7 @@ void Net<Dtype>::CompilationRuleThree(const NetParameter& param,
 }
 
 template <typename Dtype>
-void Net<Dtype>::CompilationRuleFour(const NetParameter& param,
+void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
                                      NetParameter* param_compiled) {
   // only apply this rule for inference(TEST) phase
   if (param.state().phase() != TEST || param.engine().compare("MKLDNN") != 0) {
@@ -910,7 +882,7 @@ void Net<Dtype>::CompilationRuleFour(const NetParameter& param,
                                    param,
                                    i + 1 < param.layer_size() ? i + 1 : i);
 
-      if (child_layers_params[0]->type().compare("Eltwise") == 0) {
+      if (child_layers_params.size() > 0 && child_layers_params[0]->type().compare("Eltwise") == 0) {
         std::vector<const LayerParameter*> grand_child_layers_params;
 
         Net<Dtype>::GetBlobConsumers(grand_child_layers_params,
@@ -953,6 +925,268 @@ void Net<Dtype>::CompilationRuleFour(const NetParameter& param,
   return;
 }
 
+template <typename Dtype>
+void Net<Dtype>::CompilationRuleSparse(const NetParameter& param,
+                                       NetParameter* param_compiled) {
+  //TODO: Verify the convergence of the sparse model
+  if (param.state().phase() != TEST || param.engine().compare("MKLDNN") != 0) {
+    param_compiled->CopyFrom(param);
+    return;
+  }
+
+  LayerParameter* potential_sparse_layer = NULL;
+  LayerParameter* confirmed_sparse_layer = NULL;
+  LayerParameter* layer_param = NULL;
+
+  std::map<string, string> bottom_blob_layer_mapping;
+  // top blob as the key and  its layer name as the value
+  std::map<string, string> top_blob_layer_mapping;
+  std::map<string, std::vector<LayerParameter*>> sparse_layer_name_mapping;  // key is layer name
+  std::vector<LayerParameter*> trigger_sparse_layers;
+  std::map<string, int> conv_layer_id_mapping;
+  std::map<string, int> eltwise_layer_id_mapping;
+  std::map<string, int> layer_name_id_mapping;
+
+  std::map<int, int> pooling_layer_id_stride;  // saves the layer's id which
+                                               // need to add pooling layer;
+  std::map<int, int> conv_layer_id_stride;  // saves the conv layer's id which
+                                            // need to modify its stride;
+  std::map<int, string> pooling_layer_id_top_blob;
+  
+  // step 1 get topology details, such as layer name/id mapping
+  for (int index = 0; index < param.layer_size(); index++) {
+    layer_param = (const_cast<NetParameter&>(param)).mutable_layer(index);
+    layer_name_id_mapping[layer_param->name()] = index;
+
+    for (int j = 0; j < layer_param->top_size(); j++) {
+      top_blob_layer_mapping[layer_param->top(j)] = layer_param->name();
+    }
+
+    for (int k = 0; k < layer_param->bottom_size(); k++) {
+      bottom_blob_layer_mapping[layer_param->bottom(k)] = layer_param->name();
+    }
+
+    if (layer_param->type().compare("Eltwise") == 0) {
+      eltwise_layer_id_mapping[layer_param->name()] = index;
+    }
+
+    if (layer_param->type().compare("Convolution") == 0 &&
+        layer_param->has_convolution_param() &&
+        layer_param->convolution_param().kernel_size_size() > 0 &&
+        layer_param->convolution_param().stride_size() > 0) {
+      conv_layer_id_mapping[layer_param->name()] = index;
+      
+      if (layer_param->convolution_param().kernel_size(0) > 1) {
+        potential_sparse_layer = layer_param;
+      } else if (layer_param->convolution_param().kernel_size(0) == 1 &&
+                 layer_param->convolution_param().stride(0) > 1  &&
+          (layer_param->convolution_param().pad_size() == 0 ||
+           (layer_param->convolution_param().pad_size() > 0 &&
+            layer_param->convolution_param().pad(0) == 0))) {
+        confirmed_sparse_layer = potential_sparse_layer;
+
+        if (trigger_sparse_layers.size() > 0) {
+          for (int j = 0; j < trigger_sparse_layers.size(); j++) {
+            if (top_blob_layer_mapping[trigger_sparse_layers[j]->bottom(0)] !=
+                top_blob_layer_mapping[layer_param->bottom(0)]) {
+              trigger_sparse_layers.clear();
+              break;
+            }
+          }
+          trigger_sparse_layers.push_back(layer_param);
+
+          sparse_layer_name_mapping[confirmed_sparse_layer->name()] =
+              trigger_sparse_layers;
+        } else {
+          trigger_sparse_layers.push_back(layer_param);
+        }
+      }
+    }
+  }
+
+  if(trigger_sparse_layers.size() > 1)
+    sparse_layer_name_mapping[confirmed_sparse_layer->name()] = trigger_sparse_layers;
+
+  std::map<string, std::vector<LayerParameter*>>::iterator sparse_it =
+      sparse_layer_name_mapping.begin();
+  while (sparse_it != sparse_layer_name_mapping.end() && sparse_it->second.size() > 1) {
+
+    if (sparse_it->second[0]->convolution_param().stride(0) !=
+        sparse_it->second[1]->convolution_param().stride(0)) {
+          continue;
+    }
+    LayerParameter* sparse_layer_param =
+        (const_cast<NetParameter&>(param))
+            .mutable_layer(layer_name_id_mapping[sparse_it->first]);
+    int updated_stride_value =
+        sparse_layer_param->convolution_param().stride(0) *
+        sparse_it->second[0]->convolution_param().stride(0);
+    conv_layer_id_stride[conv_layer_id_mapping[sparse_it->first]] =
+        updated_stride_value;
+    conv_layer_id_stride[conv_layer_id_mapping[sparse_it->second[0]->name()]] = 1;
+    conv_layer_id_stride[conv_layer_id_mapping[sparse_it->second[1]->name()]] = 1;
+
+    std::map<string, int>::iterator eltwise_iter = eltwise_layer_id_mapping.begin();
+    while (eltwise_iter != eltwise_layer_id_mapping.end()) {
+      // it means there is a eltwise layer between the layer need to sparse and
+      // the  layer triggers the sparse
+      if (conv_layer_id_mapping[sparse_it->first] < eltwise_iter->second &&
+          eltwise_iter->second < conv_layer_id_mapping[sparse_it->second[0]->name()]) {
+        break;  // now eltwise_iter stands for eltwise layer
+      }
+      eltwise_iter++;
+    }
+
+    std::vector<int> need_add_pooling_layer_id;
+    LayerParameter* eltwise_layer_param =
+        (const_cast<NetParameter&>(param)).mutable_layer(eltwise_iter->second);
+    for (int k = 0; k < eltwise_layer_param->bottom_size() - 1; k++) {
+      need_add_pooling_layer_id.push_back(
+          layer_name_id_mapping
+              [top_blob_layer_mapping[eltwise_layer_param->bottom(k)]]);
+      int pooling_layer_id = layer_name_id_mapping
+          [top_blob_layer_mapping[eltwise_layer_param->bottom(k)]];
+      pooling_layer_id_stride[pooling_layer_id] = updated_stride_value;
+      pooling_layer_id_top_blob[pooling_layer_id] =
+          eltwise_layer_param->bottom(k);
+    }
+    sparse_it++;
+  }
+
+  for (int i = 0; i < param.layer_size(); i++) {
+    LayerParameter* each_layer_param =
+        (const_cast<NetParameter&>(param)).mutable_layer(i);
+    if (conv_layer_id_stride.find(i) != conv_layer_id_stride.end()) {
+      each_layer_param->mutable_convolution_param()->set_stride(
+          0, conv_layer_id_stride[i]);
+    } else if (pooling_layer_id_stride.find(i) !=
+               pooling_layer_id_stride.end()) {
+      param_compiled->add_layer()->CopyFrom(*each_layer_param);
+      each_layer_param = param_compiled->add_layer();
+      each_layer_param->Clear();
+      each_layer_param->set_type("Pooling");
+      each_layer_param->set_name(pooling_layer_id_top_blob[i] + "_p");
+
+      each_layer_param->add_bottom(pooling_layer_id_top_blob[i]);
+      each_layer_param->add_top(pooling_layer_id_top_blob[i] + "_p");
+
+      each_layer_param->mutable_pooling_param()->add_stride(
+          pooling_layer_id_stride[i]);
+      each_layer_param->mutable_pooling_param()->add_kernel_size(1);
+      each_layer_param->mutable_pooling_param()->set_pool(
+          PoolingParameter_PoolMethod_MAX);
+
+      int target_layer_id = layer_name_id_mapping
+          [bottom_blob_layer_mapping[pooling_layer_id_top_blob[i]]];
+      LayerParameter* target_layer_param =
+          (const_cast<NetParameter&>(param)).mutable_layer(target_layer_id);
+      int target_blob_index = 0;
+      bool found_blob_flag = false;
+      for (; target_blob_index < target_layer_param->bottom_size();
+           target_blob_index++) {
+        if (target_layer_param->bottom(target_blob_index) ==
+            pooling_layer_id_top_blob[i]) {
+          found_blob_flag = true;
+          break;
+        }
+      }
+      if (found_blob_flag) {
+        target_layer_param->set_bottom(target_blob_index,
+                                       pooling_layer_id_top_blob[i] + "_p");
+        continue;
+      }
+    }
+    param_compiled->add_layer()->CopyFrom(*each_layer_param);
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::CompilationRuleFuseBnRelu(const NetParameter& param,
+                                    NetParameter* param_compiled) {
+  std::set<std::string> layers_to_drop;
+  for (int i = 0; i < param.layer_size(); ++i) {
+    LayerParameter* layer_param =
+          (const_cast<NetParameter&>(param)).mutable_layer(i);
+    bool layer_included = true;
+
+    // Optimization rule BnRelu:
+    // - If we are having engine MKLDNN and Relu layer within a model
+    // and input bottom comes from  BatchNorm of engine MKLDNN
+    // then we can remove Relu layer
+    // and rename BatchNorm top blob after deleted Relu's top
+    // If current layer is BatchNorm of MKLDNN engine..
+    if (((layer_param->type().compare("BatchNorm") == 0) &&
+         ((layer_param->batch_norm_param().engine() == BatchNormParameter_Engine_MKLDNN) ||
+          ((layer_param->batch_norm_param().engine() == BatchNormParameter_Engine_DEFAULT) &&
+           (layer_param->has_engine() == false)  &&
+           (param.engine().compare("MKLDNN") == 0)) ||
+          (param.engine() == "" && layer_param->engine().compare("MKLDNN") == 0)))) {
+      std::vector<const LayerParameter*> consumer_layer_params;
+      GetBlobConsumers(consumer_layer_params,
+                       layer_param->top(0),
+                       param,
+                       i+1 < param.layer_size() ? i+1 : i);
+      const LayerParameter& consumer_layer_param =
+                                    consumer_layer_params.size() > 0 ?
+                                    *(consumer_layer_params[0]) : *layer_param;
+      // Consumer layer of blob produced by BN
+      // has to be Relu layer with one Input Blob
+
+      if ((consumer_layer_param.type().compare("ReLU") == 0) &&
+          ((consumer_layer_param.relu_param().engine() == ReLUParameter_Engine_MKLDNN) ||
+           ((consumer_layer_param.relu_param().engine() == ReLUParameter_Engine_DEFAULT) &&
+            (consumer_layer_param.engine().compare(0, 6, "MKLDNN") == 0 &&
+             consumer_layer_param.engine().find(":DLA", 6) == string::npos)) ||
+           ((consumer_layer_param.relu_param().engine() == ReLUParameter_Engine_DEFAULT) &&
+            (consumer_layer_param.engine() == "") &&
+            (param.engine().compare(0, 6, "MKLDNN") == 0 &&
+             param.engine().find(":DLA", 6) == string::npos))) &&
+             !consumer_layer_param.relu_param().negative_slope()) {
+             // negative_slope should be zero
+        string& batchnorm_top_blob_name =
+            const_cast<string&>(layer_param->top(0));
+
+        if(param.state().phase() == TEST) {
+          const string& relu_top_blob_name = consumer_layer_param.top(0);
+          // Mark Consumer layer (its name) as the one marked for dropping
+          layers_to_drop.insert(consumer_layer_param.name());
+
+          // Replace BatchNorm top name with ReLU top name
+          batchnorm_top_blob_name.resize(relu_top_blob_name.size());
+          batchnorm_top_blob_name.replace(0,
+                                          relu_top_blob_name.size(),
+                                          relu_top_blob_name);
+        }
+        // set relu flag in BN
+        layer_param->mutable_batch_norm_param()->set_relu(true);
+
+        if(param.state().phase() == TRAIN) {
+          if(i+1 < param.layer_size()) {
+            LayerParameter* relu_layer_param =
+              (const_cast<NetParameter&>(param)).mutable_layer(i+1);
+            relu_layer_param->mutable_relu_param()->set_fuse(true);
+            // LOG(INFO) <<  "Bn + Relu fused." << std::endl;
+          }
+        }
+      }
+    }
+
+    if(param.state().phase() == TEST) {
+      if (layers_to_drop.find(layer_param->name()) != layers_to_drop.end()) {
+        LOG_IF(INFO, Caffe::root_solver()) << "Dropped layer: "
+               << layer_param->name() << std::endl;
+        layer_included = false;
+        // Remove dropped layer from the list of layers to be dropped
+        layers_to_drop.erase(layers_to_drop.find(layer_param->name()));
+      }
+    }
+
+    if (layer_included) {
+      param_compiled->add_layer()->CopyFrom(*layer_param);
+    }
+  }
+}
+
 template <typename Dtype>
 void Net<Dtype>::GetBlobConsumers(
                   std::vector<const LayerParameter*>& consumer_blobs,
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index cfbfdb55c..f850864ce 100755
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -709,6 +709,7 @@ message RandomAspectRatioParameter {
   optional float min_area_ratio = 1 [default = 0.5];
   optional float max_area_ratio = 2 [default = 1];
   optional float aspect_ratio_change = 3 [default = 1];
+  optional uint32 max_attempt = 4 [default = 10];
   optional ResizeParameter resize_param = 5;
 }
 
@@ -954,6 +955,7 @@ message BatchNormParameter {
   optional FillerParameter bias_filler = 8; // The filler for the bias
   // Batch size used for statistics, 0 would use the batch size of bottom blob
   optional uint32 stats_batch_size = 9 [default = 0];
+  optional bool relu = 10 [default = false];
 }
 
 message SplitParameter {
diff --git a/src/caffe/quant/quantization.cpp b/src/caffe/quant/quantization.cpp
index 2344d5e40..4429f3621 100644
--- a/src/caffe/quant/quantization.cpp
+++ b/src/caffe/quant/quantization.cpp
@@ -204,11 +204,14 @@ void Quantization::Quantize2DynamicFixedPoint() {
       else
         LOG(INFO) << "Scale param=" << scale_params_[k][0];
     } else {
-      for (int j = 0; j < il_params_[k].size(); j++) {
-        if (this->power)
+      if (this->power){
+        for (int j = 0; j < il_params_[k].size(); j++) {
           LOG(INFO) << "Integer length params[" << j << "]=" << il_params_[k][j];
-        else
+        }
+      } else{
+        for (int j = 0; j < scale_params_[k].size(); j++) {
           LOG(INFO) << "Scale params[" << j << "]=" << scale_params_[k][j];
+        }
       }
     }
   }
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 2b47b0b2e..a9892716c 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -358,10 +358,10 @@ void Solver<Dtype>::Step(int iters) {
     net_->ResetTimers();
 
 #ifdef USE_MLSL
-    if (mn::get_node_id() == 0)
+    if (mn::is_root() == true)
 #endif
-        LOG(INFO) << "iter " << iter_ << ", forward_backward_update_time: "
-                << iter_time << " ms";
+    LOG(INFO) << "iter " << iter_ << ", forward_backward_update_time: "
+            << iter_time << " ms";
 #endif
 
     // Increment the internal iter_ counter -- its value should always indicate
@@ -527,9 +527,8 @@ void Solver<Dtype>::TestClassification(const int test_net_id) {
 #ifdef USE_MLSL
     mn::allreduce(&loss, 1);
     loss /= (param_.test_iter(test_net_id) * mn::get_group_size());
-    if (mn::get_node_id() == 0) {
+    if (mn::is_root() == true)
       LOG(INFO) << "Test loss: " << loss;
-    }
 #else /* !USE_MLSL */
     loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
@@ -537,7 +536,7 @@ void Solver<Dtype>::TestClassification(const int test_net_id) {
   }
 #ifdef USE_MLSL
   mn::allreduce(test_score.data(), test_score.size());
-  if (mn::get_node_id() == 0)
+  if (mn::is_root() == true)
 #endif /* USE_MLSL */
   for (int i = 0; i < test_score.size(); ++i) {
     const int output_blob_index =
@@ -634,14 +633,13 @@ void Solver<Dtype>::TestDetection(const int test_net_id) {
   }
   if (param_.test_compute_loss()) {
 #ifdef USE_MLSL
-      mn::allreduce(&loss, 1);
-      loss /= (param_.test_iter(test_net_id) * mn::get_group_size());
-      if (mn::get_node_id() == 0) {
-          LOG(INFO) << "Test loss: " << loss;
-      }
-#else /* !USE_MLSL */
-      loss /= param_.test_iter(test_net_id);
+    mn::allreduce(&loss, 1);
+    loss /= (param_.test_iter(test_net_id) * mn::get_group_size());
+    if (mn::is_root() == true)
       LOG(INFO) << "Test loss: " << loss;
+#else /* !USE_MLSL */
+    loss /= param_.test_iter(test_net_id);
+    LOG(INFO) << "Test loss: " << loss;
 #endif /* USE_MLSL */
   }
 
@@ -690,13 +688,16 @@ void Solver<Dtype>::TestDetection(const int test_net_id) {
     mAP = allnodes_mAP;
     Dtype allnodes_num_pos = static_cast<Dtype>(num_pos.size());
     mn::allreduce(&allnodes_num_pos, 1);
-    if (mn::get_node_id() == 0)
-        mAP /= allnodes_num_pos;
+    if (mn::is_root() == true)
+      mAP /= allnodes_num_pos;
 #else /* USE_MLSL */
     mAP /= num_pos.size();
 #endif
     const int output_blob_index = test_net->output_blob_indices()[i];
     const string& output_name = test_net->blob_names()[output_blob_index];
+#ifdef USE_MLSL
+    if (mn::is_root() == true)
+#endif
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
               << mAP;
   }
diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp
index 6fd386a43..366612293 100644
--- a/src/caffe/solvers/rmsprop_solver.cpp
+++ b/src/caffe/solvers/rmsprop_solver.cpp
@@ -56,35 +56,59 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   Dtype delta = this->param_.delta();
   Dtype rms_decay = this->param_.rms_decay();
   Dtype local_rate = rate * net_params_lr[param_id];
+  Dtype momentum = this->param_.momentum();
+  const Dtype* diff_ptr = NULL;
+  Dtype* mutable_diff_ptr = NULL;
 
   switch (Caffe::mode()) {
   case Caffe::CPU:
+    if (net_params[param_id]->prv_diff()
+        && (net_params[param_id]->prv_diff_count()
+            == net_params[param_id]->count())) {
+      diff_ptr = net_params[param_id]->prv_diff();
+      mutable_diff_ptr = net_params[param_id]->mutable_prv_diff();
+    } else {
+      diff_ptr = net_params[param_id]->cpu_diff();
+      mutable_diff_ptr = net_params[param_id]->mutable_cpu_diff();
+    }
+
     // compute square of gradient in update
     caffe_powx(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_cpu_data());
+      diff_ptr, Dtype(2),
+      this->update_[param_id]->mutable_cpu_data());
 
     // update history
     caffe_cpu_axpby(net_params[param_id] -> count(),
-        Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
-        rms_decay, this->history_[param_id]-> mutable_cpu_data());
+      Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
+      rms_decay, this->history_[param_id]-> mutable_cpu_data());
 
-    // prepare update
-    caffe_powx(net_params[param_id]->count(),
-        this->history_[param_id]->cpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_cpu_data());
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+      this->history_[param_id]->cpu_data(),
+      this->update_[param_id]->mutable_cpu_data());
 
+    // add delta
     caffe_add_scalar(net_params[param_id]->count(),
-        delta, this->update_[param_id]->mutable_cpu_data());
+      delta, this->update_[param_id]->mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+      this->update_[param_id]->cpu_data(), Dtype(0.5),
+      this->update_[param_id]->mutable_cpu_data());
 
     caffe_div(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
-        this->update_[param_id]->mutable_cpu_data());
+      diff_ptr, this->update_[param_id]->cpu_data(),
+      this->update_[param_id]->mutable_cpu_data());
 
     // scale and copy
     caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->cpu_data(), Dtype(0),
-        net_params[param_id]->mutable_cpu_diff());
+      this->update_[param_id]->cpu_data(), momentum,
+      this->temp_[param_id]->mutable_cpu_data());
+
+    caffe_copy(net_params[param_id]->count(),
+      this->temp_[param_id]->cpu_data(),
+      mutable_diff_ptr);
+
     break;
   case Caffe::GPU:
 #ifndef CPU_ONLY
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index d3f1d49e6..304adf43c 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -296,9 +296,7 @@ void axpy_axpby_copy<float>(size_t count, const float decay, const float* net_pa
                             const float rate, const float momentum, float* history_data)
 {
 #ifdef _OPENMP
-//#pragma omp parallel for simd schedule(static)  //Not work for GCC 4.8
-#pragma omp parallel for schedule(static)
-#pragma simd
+#pragma omp parallel for simd schedule(static)
 #endif  
   for (size_t i = 0; i < count; ++i) {
     history_data[i] = rate * (decay * net_params_data[i] + net_params_diff[i]) + momentum * history_data[i];
@@ -311,9 +309,7 @@ void axpy_axpby_copy<double>(size_t count, const double decay, const double* net
                              const double rate, const double momentum, double* history_data)
 {
 #ifdef _OPENMP
-//#pragma omp parallel for simd schedule(static)  //Not work for GCC 4.8
-#pragma omp parallel for schedule(static)
-#pragma simd
+#pragma omp parallel for simd schedule(static)
 #endif  
   for (size_t i = 0; i < count; ++i) {
     history_data[i] = rate * (decay * net_params_data[i] + net_params_diff[i]) + momentum * history_data[i];
@@ -333,9 +329,7 @@ void axpy_axpby_copy_axpy<float>(size_t count, const float decay, float* net_par
                             const float rate, const float momentum, float* history_data, const float update_param)
 {
 #ifdef _OPENMP
-//#pragma omp parallel for simd schedule(static)  //Not work for GCC 4.8
-#pragma omp parallel for schedule(static)
-#pragma simd
+#pragma omp parallel for simd schedule(static)
 #endif  
   for (size_t i = 0; i < count; ++i) {
     history_data[i] = rate * (decay * net_params_data[i] + net_params_diff[i]) + momentum * history_data[i];
@@ -348,9 +342,7 @@ void axpy_axpby_copy_axpy<double>(size_t count, const double decay, double* net_
                              const double rate, const double momentum, double* history_data, const double update_param)
 {
 #ifdef _OPENMP
-//#pragma omp parallel for simd schedule(static)  //Not work for GCC 4.8
-#pragma omp parallel for schedule(static)
-#pragma simd
+#pragma omp parallel for simd schedule(static)
 #endif  
   for (size_t i = 0; i < count; ++i) {
     history_data[i] = rate * (decay * net_params_data[i] + net_params_diff[i]) + momentum * history_data[i];
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 612353807..507ccf150 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -56,6 +56,11 @@ SyncedMemory::~SyncedMemory() {
     cudaSetDevice(initial_device);
   }
 #endif  // CPU_ONLY
+#ifdef CO_SIM
+  if (cpu_ptr_cosim_){
+    CaffeFreeHost(cpu_ptr_cosim_, cpu_malloc_use_cuda_);
+  }
+#endif
 }
 
 inline void SyncedMemory::to_cpu() {
@@ -95,6 +100,46 @@ inline void SyncedMemory::to_cpu() {
   }
 }
 
+#ifdef CO_SIM
+//Only invoke when getting data from mkldnn engine by COSIM.
+inline void SyncedMemory::to_cpu_cosim(){
+  switch (head_) {
+  case UNINITIALIZED:
+    if (cpu_ptr_cosim_ == NULL) {
+      CaffeMallocHost(&cpu_ptr_cosim_, size_, &cpu_malloc_use_cuda_);
+      caffe_memset(size_, 0, cpu_ptr_cosim_);
+    }
+    break;
+  case HEAD_AT_GPU:
+    assert(!"Do not support GPU.");
+    break;
+  case HEAD_AT_PRV:
+    if (cpu_ptr_cosim_ == NULL) {
+      CaffeMallocHost(&cpu_ptr_cosim_, size_, &cpu_malloc_use_cuda_);
+    }
+    CHECK(prv_descriptor_.get());
+    prv_descriptor_->convert_from_prv_cosim(cpu_ptr_cosim_);
+    prv_descriptor_->on_to_cpu();
+    break;
+  case SYNCED_PRV:
+  case HEAD_AT_CPU:
+  case SYNCED:
+    if (cpu_ptr_cosim_ == NULL) {
+      CaffeMallocHost(&cpu_ptr_cosim_, size_, &cpu_malloc_use_cuda_);
+    }
+    memcpy(cpu_ptr_cosim_, cpu_ptr_, size_);
+    break;
+  }
+}
+
+const void* SyncedMemory::cpu_data_cosim() {
+  boost::mutex::scoped_lock lock(mtx);
+  to_cpu_cosim();
+  return (const void*)cpu_ptr_cosim_;
+
+}
+#endif
+
 inline void SyncedMemory::to_gpu() {
 #ifndef CPU_ONLY
   switch (head_) {
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index f68be835c..bf685a7df 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -345,7 +345,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       } else if (solver_->type() == string("RMSProp")) {
         const Dtype rms_decay = 0.95;
         update_value /= std::sqrt(rms_decay*history_value
-            + grad * grad * (1 - rms_decay)) + delta_;
+            + grad * grad * (1 - rms_decay) + delta_);
       } else if (solver_->type() == string("AdaDelta")) {
         const Dtype update_history_value = (i == D) ?
             history[1 + num_param_blobs]->cpu_data()[0] :
@@ -1250,6 +1250,7 @@ class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
     const Dtype rms_decay = 0.95;
     SolverParameter new_param = param;
     new_param.set_rms_decay(rms_decay);
+    new_param.set_momentum(0);
     this->solver_.reset(new RMSPropSolver<Dtype>(new_param));
   }
 };
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 257ae9153..9d2e11340 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -52,23 +52,12 @@ void LMDB::Open(const string& source, Mode mode) {
   int flags = 0;
   if (mode == READ) {
     flags = MDB_RDONLY | MDB_NOTLS;
+#ifdef ALLOW_LMDB_NOLOCK
+    flags |= MDB_NOLOCK;
+#endif
   }
   int rc = mdb_env_open(mdb_env_, source.c_str(), flags, 0664);
-#ifndef ALLOW_LMDB_NOLOCK
   MDB_CHECK(rc);
-#else
-  if (rc == EACCES) {
-    LOG(WARNING) << "Permission denied. Trying with MDB_NOLOCK ...";
-    // Close and re-open environment handle
-    mdb_env_close(mdb_env_);
-    MDB_CHECK(mdb_env_create(&mdb_env_));
-    // Try again with MDB_NOLOCK
-    flags |= MDB_NOLOCK;
-    MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
-  } else {
-    MDB_CHECK(rc);
-  }
-#endif
   LOG(INFO) << "Opened lmdb " << source;
 }
 
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 5edfaa88a..f22af5e11 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -730,8 +730,18 @@ int main(int argc, char** argv) {
   caffe::GlobalInit(&argc, &argv);
 #ifdef USE_MLSL
   caffe::mn::nGroup = FLAGS_n_group;
+  if (caffe::mn::nGroup <= 0) {
+      LOG(ERROR) << "Invalid number of group: " << caffe::mn::nGroup;
+      return 1;
+  }
+
   caffe::mn::nServer = FLAGS_n_server;
   caffe::mn::init(&argc, &argv);
+  if (caffe::mn::get_group_size() <= 0) {
+      LOG(ERROR) << "Invalid group size: " << caffe::mn::get_group_size();
+      return 1;
+  }
+
   CHECK_EQ(caffe::mn::get_world_size(),
            caffe::mn::nGroup * caffe::mn::get_group_size() + caffe::mn::nServer);
   if (caffe::mn::nGroup > 1) {
diff --git a/tools/extra/DFA_multi_parser_time.py b/tools/extra/DFA_multi_parser_time.py
new file mode 100644
index 000000000..58b1d0cb2
--- /dev/null
+++ b/tools/extra/DFA_multi_parser_time.py
@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+# 
+# All modification made by Intel Corporation: Copyright (c) 2016 Intel Corporation
+# 
+# All contributions by the University of California:
+# Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
+# All rights reserved.
+# 
+# All other contributions:
+# Copyright (c) 2014, 2015, the respective contributors
+# All rights reserved.
+# For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md
+# 
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+#     * Redistributions of source code must retain the above copyright notice,
+#       this list of conditions and the following disclaimer.
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#     * Neither the name of Intel Corporation nor the names of its contributors
+#       may be used to endorse or promote products derived from this software
+#       without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+
+from extract_seconds import extract_datetime_from_line, get_log_created_year
+
+class time_DFA:
+    def __init__(self,log_path):
+        self.filepath = log_path
+        self.test_time = []
+        self.snapshot_time = []
+        self.total_test_time = 0.0
+        self.total_snapshot_time = 0.0
+        self.total_time = 0.0
+        self.log_year = get_log_created_year(log_path)
+
+    def parse_time(self):
+        f = open(self.filepath,'r')
+        line = ''
+        while True:
+            self.prev_line = line
+            line = f.readline()
+
+            if not line:
+                if self.total_time == 0 and begin_time and self.prev_line:
+                    quit_time = extract_datetime_from_line(self.prev_line, self.log_year)
+                    self.total_time = (quit_time - begin_time).total_seconds()
+                break
+
+            if  '] Solving' in line:
+                begin_time = extract_datetime_from_line(line, self.log_year)
+
+            if '] Optimization Done.' in line:
+                quit_time = extract_datetime_from_line(line, self.log_year)
+                self.total_time = (quit_time-begin_time).total_seconds()
+                break
+
+            if '] Iteration' in line and 'lr' in line:
+                self.train_state(f)
+
+            elif 'Testing net ' in line:
+                start_time = extract_datetime_from_line(line, self.log_year)
+                iter_num = line.split(' ')[-4]
+                end_time = self.test_state(f)
+                self.test_time.append({'config':iter_num,'time':(end_time-start_time).total_seconds()})
+                self.total_test_time += (end_time-start_time).total_seconds()
+
+            elif '] Snapshotting to binary proto' in line:
+                start_time = extract_datetime_from_line(self.prev_line, self.log_year)
+                end_time = self.snapshot_state(f)
+                if end_time:
+                    self.total_snapshot_time += (end_time - start_time).total_seconds()
+
+            elif '] Snapshot begin' in line:
+                start_time = extract_datetime_from_line(line, self.log_year)
+                end_time = self.accurate_snapshot_state(f)
+                if end_time:
+                    self.total_snapshot_time += (end_time - start_time).total_seconds()
+
+    def test_state(self, f):
+        line = f.readline()
+        while line:
+            if 'Test net output' not in line and 'Test net output' in self.prev_line:
+                return extract_datetime_from_line(self.prev_line, self.log_year)
+            else:
+                self.prev_line = line
+                line = f.readline()
+        return extract_datetime_from_line(self.prev_line, self.log_year)
+
+    def train_state(self,f):
+        pass
+
+    def snapshot_state(self,f):
+        line = f.readline()
+        while line:
+            if '] Snapshotting solver state' in line:
+                return extract_datetime_from_line(line, self.log_year)
+            else:
+                self.prev_line = line
+                line = f.readline()
+        return extract_datetime_from_line(self.prev_line, self.log_year)
+
+    def accurate_snapshot_state(self,f):
+        line = f.readline()
+        while line:
+            if '] Snapshot end' in line:
+                return extract_datetime_from_line(line, self.log_year)
+            else:
+                self.prev_line = line
+                line = f.readline()
+        return extract_datetime_from_line(self.prev_line, self.log_year)
diff --git a/tools/extra/README.md b/tools/extra/README.md
new file mode 100644
index 000000000..7cd30b946
--- /dev/null
+++ b/tools/extra/README.md
@@ -0,0 +1,7 @@
+### Caffe_log_parser
+The tool caffe_log_parser.py is used to parse both single-node and multi-node training log.
+It produce the time to train, , loss trend, learning rate trend and test accuracy trend.
+### Usage:
+```bash
+$CAFFE_HOME/tools/extra/caffe_log_parser.py <your_log_path>
+```
diff --git a/tools/extra/caffe_log_parser.py b/tools/extra/caffe_log_parser.py
new file mode 100755
index 000000000..997f153e3
--- /dev/null
+++ b/tools/extra/caffe_log_parser.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# 
+# All modification made by Intel Corporation: Copyright (c) 2016 Intel Corporation
+# 
+# All contributions by the University of California:
+# Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
+# All rights reserved.
+# 
+# All other contributions:
+# Copyright (c) 2014, 2015, the respective contributors
+# All rights reserved.
+# For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md
+# 
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+#     * Redistributions of source code must retain the above copyright notice,
+#       this list of conditions and the following disclaimer.
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#     * Neither the name of Intel Corporation nor the names of its contributors
+#       may be used to endorse or promote products derived from this software
+#       without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+
+import os
+import re
+from DFA_multi_parser_time import time_DFA
+import argparse
+import log_plot
+
+parser = argparse.ArgumentParser(description='caffe log parser for training time and plot.')
+parser.add_argument('--time',default = True, action = 'store_true')
+parser.add_argument('log_file',  help='caffe training log file')
+parser.add_argument('--out_dir',help='relavent file output path',default='./img_out')
+parser.add_argument('--plot', default = True, help=' --plot draw graph( Iter vs LR, Iter vs Loss and Iter vs acc) \n ', action = 'store_true')
+args = parser.parse_args()
+
+
+def cut_multi_log(path_to_multi_log):
+    multi_flag = False
+    pattern = re.compile('^\[0\]')
+    file_content = []
+    with open(path_to_multi_log,'r') as f:
+        for line in f:
+            m = pattern.match(line)
+            if m:
+                file_content.append(line[3:])
+                multi_flag = True
+    if multi_flag:
+        log_base_name = os.path.basename(path_to_multi_log)
+        cuted_log_path = os.path.join('/tmp/', log_base_name+'-0')
+        with open(cuted_log_path, 'w') as f:
+            for line in file_content:
+                f.write(line)
+            f.close()
+        return cuted_log_path
+    else:
+        return path_to_multi_log
+
+
+def main():
+    log_path = args.log_file
+    out_dir = args.out_dir
+    log_path_cuted = cut_multi_log(log_path)
+    if args.time:
+        test = time_DFA(log_path_cuted)
+        test.parse_time()
+        print 'Total time(hour) : ',test.total_time/60.0/60.0
+        print 'Total test time(hour) :',test.total_test_time/60.0/60.0
+        print 'Total snapshot time(hour) : ',test.total_snapshot_time/60.0/60.0
+        print 'Total train time(hour) : ',(test.total_time - test.total_test_time - test.total_snapshot_time)/60.0/60.0
+
+    if args.plot:
+        if not os.path.exists(out_dir):
+            os.makedirs(out_dir)
+        log_plot.plot_train_graph(log_path_cuted, out_dir)
+        log_plot.plot_test_graph(log_path_cuted, out_dir)
+        print 'Plot train/test graph saved at : ', out_dir
+
+
+if __name__ == '__main__':
+	main()
+
+
diff --git a/tools/extra/extract_seconds.py b/tools/extra/extract_seconds.py
index 0ea8be391..6a06fbda6 100755
--- a/tools/extra/extract_seconds.py
+++ b/tools/extra/extract_seconds.py
@@ -41,17 +41,23 @@
 
 def extract_datetime_from_line(line, year):
     # Expected format: I0210 13:39:22.381027 25210 solver.cpp:204] Iteration 100, lr = 0.00992565
+    raw_line = line
     line = line.strip().split()
-    month = int(line[0][1:3])
-    day = int(line[0][3:])
-    timestamp = line[1]
-    pos = timestamp.rfind('.')
-    ts = [int(x) for x in timestamp[:pos].split(':')]
-    hour = ts[0]
-    minute = ts[1]
-    second = ts[2]
-    microsecond = int(timestamp[pos + 1:])
-    dt = datetime.datetime(year, month, day, hour, minute, second, microsecond)
+    try:
+        month = int(line[0][1:3])
+        day = int(line[0][3:])
+        timestamp = line[1]
+        pos = timestamp.rfind('.')
+        ts = [int(x) for x in timestamp[:pos].split(':')]
+        hour = ts[0]
+        minute = ts[1]
+        second = ts[2]
+        microsecond = int(timestamp[pos + 1:])
+        dt = datetime.datetime(year, month, day, hour, minute, second, microsecond)
+    except ValueError:
+        print "Unexpected error when parsing line:"
+        print raw_line
+        sys.exit(1)
     return dt
 
 
diff --git a/tools/extra/log_plot.py b/tools/extra/log_plot.py
new file mode 100755
index 000000000..eb3d395a7
--- /dev/null
+++ b/tools/extra/log_plot.py
@@ -0,0 +1,164 @@
+#!/usr/bin/env python
+# 
+# All modification made by Intel Corporation: Copyright (c) 2016 Intel Corporation
+# 
+# All contributions by the University of California:
+# Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
+# All rights reserved.
+# 
+# All other contributions:
+# Copyright (c) 2014, 2015, the respective contributors
+# All rights reserved.
+# For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md
+# 
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+#     * Redistributions of source code must retain the above copyright notice,
+#       this list of conditions and the following disclaimer.
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#     * Neither the name of Intel Corporation nor the names of its contributors
+#       may be used to endorse or promote products derived from this software
+#       without specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+
+import matplotlib as mpl
+mpl.use('Agg')
+import os
+import sys
+import re
+import random
+import matplotlib.pyplot as plt
+
+
+def cut_multi_log(path_to_multi_log):
+    logfile_dict = {}
+    multi_flag = False
+    pattern = re.compile('\[\d\]')
+    with open(path_to_multi_log,'r') as f:
+        for line in f:
+            m = pattern.match(line)
+            if m:
+                multi_flag = True
+                num = m.group()[1]
+                if num in logfile_dict.keys():
+                    file_data = logfile_dict[num]
+                    file_data.append(line[3:])
+                else:
+                    file_data = []
+                    file_data.append(line[3:])
+                    logfile_dict[num] = file_data
+
+        log_name_list = []
+        for key in logfile_dict.keys():
+            log_base_name = os.path.basename(path_to_multi_log)
+            new_log_file_name = log_base_name + '-'+key
+            file_data=logfile_dict[key]
+            log_name_list.append(new_log_file_name)
+            with open(new_log_file_name,'w') as f:
+                for item in file_data:
+                    f.write(item)
+
+    if multi_flag == True:
+        return log_name_list
+    else:
+        return [path_to_multi_log]
+
+def lr_exist(filename):
+    with open(filename,'r') as f:
+        for line in f:
+            line = line.strip()
+            if line[0] != '#':
+                line_list = line.split()
+                if len(line_list) < 4:
+                    return False
+                else:
+                    return True
+
+def plot_train_graph(logpath,png_path):
+    get_data(logpath)
+    basename = os.path.basename(logpath)
+    train_file = basename+'.train'
+    loss_data = get_plot_data(train_file,0,2)
+    linewidth = 0.75
+    color = [random.random(), random.random(), random.random()]
+
+    plt.plot(loss_data[0], loss_data[1], label = 'Loss', color = color,marker = 'o', linewidth = linewidth,markersize=2)
+    plt.title('Training Graph: Iter vs Loss')
+    plt.xlabel('Iteration')
+    plt.ylabel('Loss')
+    loss_path = os.path.join(png_path,basename+'.loss.png')
+    plt.savefig(loss_path)
+    plt.show()
+
+    if not lr_exist(train_file):
+        print "no learning rate data"
+        return
+
+    LR_data = get_plot_data(train_file,0,3)
+    plt.clf()
+    color = [random.random(), random.random(), random.random()]
+    plt.plot(LR_data[0], LR_data[1], label = 'Learning Rate', color = color,marker = 'o', linewidth = linewidth,markersize=2)
+    plt.title('Training Graph: Iter vs Learning Rate')
+    plt.xlabel('Iteration')
+    plt.ylabel('Learning rate')
+    lr_path = os.path.join(png_path,basename+'.lr.png')
+    plt.savefig(lr_path)
+    plt.show()
+
+def plot_test_graph(logpath,png_path):
+    basename = os.path.basename(logpath)
+    get_data(logpath)
+    test_file = basename+'.test'
+    acc_data = get_plot_data(test_file,0,2)
+    linewidth = 0.75
+    color = [random.random(), random.random(), random.random()]
+    plt.clf()
+    plt.plot(acc_data[0], acc_data[1], label = 'accuracy', color = color,marker = 'o', linewidth = linewidth)
+    plt.title('Test Graph: Accuracy')
+    plt.xlabel('Iteration')
+    plt.ylabel('acc')
+    test_path = os.path.join(png_path,basename+'.acc.png')
+    plt.savefig(test_path)
+    plt.show()
+
+def get_plot_data(filepath,idx1,idx2):
+    data = [[],[]]
+    base_line_flag = True
+    base_length = 4
+    with open(filepath,'r') as f:
+        for line in f:
+            line = line.strip()
+            if line[0] != '#':
+                line_list = line.split()
+                if base_line_flag == True:
+                    base_line_flag = False
+                    base_length = len(line_list)
+
+                if len(line_list) < base_length:
+                    return data
+                data[0].append(float(line_list[idx1].strip()))
+                data[1].append(float(line_list[idx2].strip()))
+    return data
+
+def get_data(logpath):
+    sh_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                           'parse_log.sh')
+    if os.system('{} {}'.format(sh_path, logpath)):
+        sys.exit(1)
+
+
diff --git a/tools/extra/plot_loss_trends.py b/tools/extra/plot_loss_trends.py
new file mode 100644
index 000000000..80fc611ef
--- /dev/null
+++ b/tools/extra/plot_loss_trends.py
@@ -0,0 +1,238 @@
+import os
+import re
+import argparse
+from collections import defaultdict
+import random
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.legend as lgd
+import matplotlib.markers as mks
+import pdb
+
+class TrainLog(object):
+    '''train log file analyze and data collection'''
+    def __init__(self, log_files):
+        '''init func'''
+        self._log_files = log_files
+        self._multi_losses = defaultdict(list)
+        self._multi_lrs = defaultdict(list)
+        self._multi_iterations = defaultdict(list)
+        self._multi_mbox_losses = defaultdict(list)
+        self._multi_test_iterations = defaultdict(list)
+        self._multi_detection_accuracies = defaultdict(list)
+        self._parse_log()
+        self._is_detection = True if len(self._multi_mbox_losses) > 0 else False
+
+    def _parse_log(self):
+        '''parse log files to obtain loss and iteration data'''
+        def obtain_log_name(log_file):
+            '''get log name from log file'''
+            if not os.path.isfile(log_file):
+                raise Exception('Error: log file {} does not exist.'.format(self.log_file))
+            #Might need to change this to have a better representation in the future, so keep it here
+            return log_file
+            
+        def obtain_process_id(line):
+            '''get process id from line string'''
+            process_id_pattern = re.compile('\[\d+\]')
+            process_id_list = process_id_pattern.findall(line)
+            return '0' if len(process_id_list) == 0 else process_id_list[0][1:-1]
+
+        loss_pattern = re.compile('.* Iteration \d+, loss = .*')
+        lr_pattern = re.compile('.* Iteration \d+, lr = .*')
+        mbox_loss_pattern = re.compile('.* Train net output #\d+: mbox_loss = .*')
+        detection_accuracy_pattern = re.compile('.* Test net output #\d+: detection_eval = .*')
+        test_iteration_pattern = re.compile('.* Iteration \d+, Testing net \(#\d+\).*')
+        for log_file in self._log_files:
+            log_name = obtain_log_name(log_file)
+            with open(log_file, 'r') as f:
+                for line in f.readlines():
+                    if re.match(loss_pattern, line):
+                        if '0' != obtain_process_id(line):
+                            continue
+                        self._multi_losses[log_name].append(float(line.split()[-1]))
+                        self._multi_iterations[log_name].append(int(line.split()[-4][:-1]))
+                    elif re.match(lr_pattern, line):
+                        if '0' != obtain_process_id(line):
+                            continue
+                        self._multi_lrs[log_name].append(float(line.split()[-1]))
+                    elif re.match(mbox_loss_pattern, line):
+                        if '0' != obtain_process_id(line):
+                            continue
+                        self._multi_mbox_losses[log_name].append(float(line.split()[-2]))
+                    elif re.match(test_iteration_pattern, line):
+                        if '0' != obtain_process_id(line):
+                            continue
+                        self._multi_test_iterations[log_name].append(float(line.split()[-4][:-1]))
+                    elif re.match(detection_accuracy_pattern, line):
+                        if '0' != obtain_process_id(line):
+                            continue
+                        self._multi_detection_accuracies[log_name].append(float(line.split()[-1]))
+
+    @property
+    def is_detection(self):
+        '''check if current train log is generated by detection or classification'''
+        return self._is_detection
+
+    @property
+    def iterations(self):
+        '''get iterations showed within log file'''
+        return self._multi_iterations
+
+    @property
+    def test_iterations(self):
+        '''get test iterations showed within log file'''
+        return self._multi_test_iterations
+
+    @property
+    def losses(self):
+        '''get losses data showed within log file'''
+        return self._multi_losses
+
+    @property
+    def mbox_losses(self):
+        '''get mbox losses data showed within log file'''
+        return self._multi_mbox_losses
+
+    @property
+    def lrs(self):
+        '''get lrs data showed within log file'''
+        return self._multi_lrs
+
+    @property
+    def detection_accuracies(self):
+        '''get detection accuracies data showed within log file'''
+        return self._multi_detection_accuracies
+
+class PlotTrend(object):
+    '''plot single or multi trends within a fig'''
+    def __init__(self, y_items, x_items, out_dir):
+        '''init func'''
+        self.y_items = y_items
+        self.x_items = x_items
+        self.y_axis_name = y_items.keys()[0]
+        self.x_axis_name = x_items.keys()[0]
+        self.y_dict = y_items.values()[0]
+        self.x_dict = x_items.values()[0]
+        self.out_dir = out_dir
+
+    def get_chart_type_description(self):
+        '''get chart type description'''
+        y_field, x_field = self.y_axis_name, self.x_axis_name
+        chart_type_desc_separator = ' vs. '
+        return chart_type_desc_separator.join([y_field, x_field])
+
+    def get_legend_loc(self):
+        '''get legend loc'''
+        loc = 'lower right'
+        if self.y_axis_name.find('accuracy') != -1:
+            loc = 'lower right'
+        elif self.y_axis_name.find('loss') != -1 or self.y_axis_name.find('lr') != -1:
+            loc = 'upper right'
+        return loc
+
+    def result_png_file(self, y_field):
+        '''generate result png file based on y field name'''
+        return self.out_dir + y_field + '_plot.png'
+
+    def random_marker(self):
+        '''return random marker for plot'''
+        markers = mks.MarkerStyle.markers
+        num = len(markers.values())
+        idx = random.randint(0, num - 1)
+        return markers.values()[idx]
+
+    def get_data_label(self, log_name):
+        '''get data source label'''
+        label = log_name
+        return label
+
+    def plot(self):
+        '''plot trends'''
+        if len(self.y_dict) != len(self.x_dict):
+            raise Exception("Error: y_dict and x_dict are not having equal size.")
+        self.plot_multiple_trends(self.y_dict, self.x_dict)
+
+    def plot_multiple_trends(self, y_dict, x_dict):
+        '''plot y_items and x_items into a single fig'''
+        for key in y_dict:
+            y_list, x_list = y_dict[key], x_dict[key]
+            # Within current log, number of iterations is 1 more than number of lrs
+            if len(y_list) != len(x_list):
+                if len(y_list) < len(x_list) and len(y_list) > 0:
+                    x_list = x_list[:len(y_list)]
+                else:
+                    raise Exception('Error: y list has distinct length with x list')
+            color = [random.random(), random.random(), random.random()]
+            linewidth = 0.75
+            label = self.get_data_label(key)
+            plt.plot(x_list, y_list, label = label, color = color, 
+                     linewidth = linewidth)
+            legend_loc = self.get_legend_loc()
+            plt.legend(loc = legend_loc, ncol = 1) # adjust ncol to fit the space
+            plt.title(self.get_chart_type_description())
+            plt.xlabel(self.x_axis_name)
+            plt.ylabel(self.y_axis_name)
+        png_file = self.result_png_file(self.y_axis_name)
+        plt.savefig(png_file)
+        plt.show()
+
+def plot_loss_trends(train_log, out_dir):
+    '''plot loss trends of train logs'''
+    y_items = {'loss' : train_log.losses}
+    x_items = {'Iterations' : train_log.iterations}
+    #pdb.set_trace()
+    plot_trend = PlotTrend(y_items, x_items, out_dir)
+    plot_trend.plot()
+
+def plot_mbox_loss_trends(train_log, out_dir):
+    '''plot mbox loss trends of train logs'''
+    if not train_log.is_detection: return
+    y_items = {'mbox loss' : train_log.mbox_losses}
+    x_items = {'Iterations' : train_log.iterations}
+    plot_trend = PlotTrend(y_items, x_items, out_dir)
+    plot_trend.plot()
+
+def plot_lr_trends(train_log, out_dir):
+    '''plot lrs trend of train logs'''
+    y_items = {'lr' : train_log.lrs}
+    x_items = {'Iterations' : train_log.iterations}
+    plot_trend = PlotTrend(y_items, x_items, out_dir)
+    plot_trend.plot()
+
+def plot_detection_accuracy_trends(train_log, out_dir):
+    '''plot detection accuracy trend of train logs'''
+    if not train_log.is_detection: return
+    y_items = {'detection accuracy' : train_log.detection_accuracies}
+    x_items = {'Test iterations' : train_log.test_iterations}
+    plot_trend = PlotTrend(y_items, x_items, out_dir)
+    plot_trend.plot()
+
+def parse_args():
+    '''parse program arguments'''
+    description = ('Parse Caffe training log and plot loss trends,\
+                    Before running it, you need to install tkinter package,\
+                    python plot_loss_trends.py -l/--log_files your/log/files \
+                    [-o/--output_dir your/output/directory, default is your pwd directory]')
+    parser = argparse.ArgumentParser(description = description)
+    parser.add_argument('-l', '--log_files',
+                        nargs = '+',
+                        help = 'caffe training log file path')
+    parser.add_argument('-o', '--output_dir',
+                        default = './',
+                        help = 'Directory in which to place output result files')
+    args = parser.parse_args()
+    return args
+
+def main():
+    '''main routine'''
+    args = parse_args()
+    print args
+    train_log = TrainLog(args.log_files)
+    plot_loss_trends(train_log, args.output_dir)
+    plot_mbox_loss_trends(train_log, args.output_dir)
+    plot_lr_trends(train_log, args.output_dir)
+    plot_detection_accuracy_trends(train_log, args.output_dir)
+
+if __name__ == '__main__':
+    main()