resnet-50-pytorch

Use Case and High-Level Description

Resnet 50 is image classification model pretrained on ImageNet dataset. This is PyTorch implementation based on architecture described in paper "Deep Residual Learning for Image Recognition" in TorchVision package (see here).

The model input is a blob that consists of a single image of "1x3x224x224" in RGB order.

The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.

Example

Specification

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

Accuracy

Metric Value
Top 1 76.128%
Top 5 92.858%

Performance

Input

Original model

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

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

Converted model

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

Channel order is BGR

Output

Original model

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

Converted model

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

Legal Information

The original model is distributed under the following license:

BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
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 the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
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