Create README.md

darknet/README.md

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+## Model Zoo
+
+| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | AP<sub>S</sub><sup>test</sup> | AP<sub>M</sub><sup>test</sup> | AP<sub>L</sub><sup>test</sup> | batch1 throughput |
+| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
+| **YOLOv4-CSP** | 640 | 48.8% | **67.5%** | 53.1% | **33.1%** | 54.1% | 62.3% | 76 *fps* |
+| **YOLOR-CSP** | 640 | **49.1%** | **67.5%** | **53.5%** | 32.5% | **54.3%** | **62.6%** | - |
+|  |  |  |  |  |  |  |
+
+## Installation
+
+https://github.com/AlexeyAB/darknet
+
+Docker environment (recommended)
+<details><summary> <b>Expand</b> </summary>
+
+```
+# get code
+git clone https://github.com/AlexeyAB/darknet
+
+# create the docker container, you can change the share memory size if you have more.
+nvidia-docker run --name yolor -it -v your_coco_path/:/coco/ -v your_code_path/:/yolor --shm-size=64g nvcr.io/nvidia/pytorch:21.02-py3
+
+# apt install required packages
+apt update
+apt install -y libopencv-dev
+
+# edit Makefile
+#GPU=1
+#CUDNN=1
+#CUDNN_HALF=1
+#OPENCV=1
+#AVX=1
+#OPENMP=1
+#LIBSO=1
+#ZED_CAMERA=0
+#ZED_CAMERA_v2_8=0
+#
+#USE_CPP=0
+#DEBUG=0
+#
+#ARCH= -gencode arch=compute_52,code=[sm_70,compute_70] \
+#      -gencode arch=compute_61,code=[sm_75,compute_75] \
+#      -gencode arch=compute_61,code=[sm_80,compute_80] \
+#      -gencode arch=compute_61,code=[sm_86,compute_86]
+#
+#...
+
+# build
+make -j8
+```
+
+</details>
+
+## Testing
+
+To reproduce inference speed, using:
+
+```
+CUDA_VISIBLE_DEVICES=0 ./darknet detector demo cfg/coco.data cfg/yolov4-csp.cfg weights/yolov4-csp.weights source/test.mp4 -dont_show -benchmark 
+```