Code and model for the paper:

[DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](http://arxiv.org/abs/1606.06160), by Zhou et al.

We hosted a demo at CVPR16 on behalf of Megvii, Inc, running a real-time 1/4-VGG size DoReFa-Net on ARM and half-VGG size DoReFa-Net on FPGA.
We're not planning to release our C++ runtime for bit-operations.
In this repo, bit operations are performed through `tf.float32`.

Pretrained model for (1,4,32)-ResNet18 and (1,2,6)-AlexNet are available at
[google drive](https://drive.google.com/a/megvii.com/folderview?id=0B308TeQzmFDLa0xOeVQwcXg1ZjQ).
They're provided in the format of numpy dictionary, so it should be very easy to port into other applications.
The __binary-weight 4-bit-activation ResNet-18__ model has 59.2% top-1 validation accuracy.

Note that when (W,A,G) is set to (1,32,32), this code is also an implementation of [Binary Weight Network](https://arxiv.org/abs/1511.00363).
But with (W,A,G) set to (1,1,32), it is not equivalent to [XNOR-Net](https://arxiv.org/abs/1603.05279), although it won't be hard to implement it.

Alternative link to this page: [http://dorefa.net](http://dorefa.net)

## Preparation:

To use the script. You'll need:

+ TensorFlow >= 0.12.1

+ OpenCV bindings for Python

+ [tensorpack](https://github.com/ppwwyyxx/tensorpack):

```
git clone https://github.com/ppwwyyxx/tensorpack
pip install --user -r tensorpack/requirements.txt
pip install --user scipy
export PYTHONPATH=$PYTHONPATH:`readlink -f tensorpack`
```

+ Look at the docstring in `*-dorefa.py` to see detailed usage and performance.

## Support

Please use [github issues](https://github.com/ppwwyyxx/tensorpack/issues) for any issues related to the code itself.
Send email to the authors for general questions related to the paper.

## Citation

If you use our code or models in your research, please cite:
```
@article{zhou2016dorefa,
  author    = {Shuchang Zhou and Yuxin Wu and Zekun Ni and Xinyu Zhou and He Wen and Yuheng Zou},
  title     = {DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients},
  journal   = {CoRR},
  volume    = {abs/1606.06160},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.06160},
}
```
