https://github.com/836304831/tensorflow-resnet

ResNet model in TensorFlow

https://github.com/836304831/tensorflow-resnet

Repository

ResNet model in TensorFlow

https://github.com/836304831/tensorflow-resnet/blob/master/

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](http://arxiv.org/abs/1512.03385).  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](https://github.com/KaimingHe/deep-residual-networks). The `convert.py`
  will convert the weights for use with TensorFlow.

* Implemented in the style of
  [Inception](https://github.com/tensorflow/models/tree/master/inception/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](https://arxiv.org/abs/1603.09382), [shared weights at each
  scale](https://arxiv.org/abs/1604.03640), 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](https://raw.githubusercontent.com/ry/tensorflow-resnet/master/data/tensorflow-resnet-pretrained-20160509.tar.gz.torrent)  464M


## Notes

* This code depends on [TensorFlow git commit
  cf7ce8](https://github.com/tensorflow/tensorflow/commit/cf7ce8a7879b6a7ba90441724ea3f8353917a515)
  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.

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