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README
MIT

This code is very old and doesn't run on modern TensorFlow. Indeed, TF now includes a resnet model in its core library: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py

Also checkout my new ML project at http://propelml.org/

ResNet in TensorFlow

Implemenation of Deep Residual Learning for Image Recognition. Includes a tool to use He et al's published trained Caffe weights in TensorFlow.

MIT license. Contributions welcome.

Goals

  • Be able to use the pre-trained model's that Kaiming He has provided for Caffe. The convert.py will convert the weights for use with TensorFlow.

  • Implemented in the style of Inception not using any classes and making heavy use of variable scope. It should be easily usable in other models.

  • Foundation to experiment with changes to ResNet like stochastic depth, shared weights at each scale, and 1D convolutions for audio. (Not yet implemented.)

  • ResNet is fully convolutional and the implementation should allow inputs to be any size.

  • Be able to train out of the box on CIFAR-10, 100, and ImageNet. (Implementation incomplete)

Pretrained Model

To convert the published Caffe pretrained model, run convert.py. However Caffe is annoying to install so I'm providing a download of the output of convert.py:

tensorflow-resnet-pretrained-20160509.tar.gz.torrent 464M

Notes

  • This code depends on TensorFlow git commit cf7ce8 or later because ResNet needs 1x1 convolutions with stride 2. TF 0.8 is not new enough.

  • The convert.py script checks that activations are similiar to the caffe version but it's not exactly the same. This is probably due to differences between how TF and Caffe handle padding. Also preprocessing is done with color-channel means instead of pixel-wise means.

The MIT License (MIT) Copyright (c) 2016 Ryan Dahl Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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