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

ResNet model in TensorFlow
https://github.com/ry/tensorflow-resnet

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ResNet model in TensorFlow

• Host: GitHub
• URL: https://github.com/ry/tensorflow-resnet
• Owner: ry
• License: mit
• Archived: true
• Created: 2015-12-18T19:03:22.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2018-06-16T02:54:27.000Z (over 6 years ago)
• Last Synced: 2024-05-10T09:31:26.709Z (7 months ago)
• Language: Python
• Homepage:
• Size: 1.36 MB
• Stars: 1,649
• Watchers: 57
• Forks: 627
• Open Issues: 38
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-image-classification - unofficial-tensorflow : https://github.com/ry/tensorflow-resnet
• awesome-image-classification - unofficial-tensorflow : https://github.com/ry/tensorflow-resnet

README

        
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.