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resnet-50-caffe2

Use Case and High-Level Description

This is a Caffe2* version of the ResNet-50 model, designed to perform image classification. This model was converted from Caffe* to Caffe2* format. For details see repository https://github.com/facebookarchive/models/tree/master/resnet50, paper https://arxiv.org/abs/1512.03385.

Specification

Metric Value
Type Classification
GFLOPs 8.216
MParams 25.53
Source framework Caffe2*

Accuracy

Metric Value
Top 1 76.38%
Top 5 93.188%

Input

Original model

Image, name - gpu_0/data, shape - 1,3,224,224, format is B,C,H,W where:

  • B - batch size
  • C - channel
  • H - height
  • W - width

Channel order is BGR. Mean values - [103.53,116.28,123.675], scale values - [57.375,57.12,58.395].

Converted model

Image, name - gpu_0/data, shape - 1,3,224,224, format is B,C,H,W where:

  • B - batch size
  • C - channel
  • H - height
  • W - width

Channel order is BGR.

Output

Original model

Object classifier according to ImageNet classes, name - gpu_0/softmax, shape - 1,1000, output data format is B,C where:

  • B - batch size
  • C - predicted probabilities for each class in [0, 1] range

Converted model

Object classifier according to ImageNet classes, name - gpu_0/softmax, shape - 1,1000, output data format is B,C where:

  • B - batch size
  • C - predicted probabilities for each class in [0, 1] range

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.