README.md

Code and model for the paper:

DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients, 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. 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. But with (W,A,G) set to (1,1,32), it is not equivalent to XNOR-Net, although it won't be hard to implement it.

Alternative link to this page: http://dorefa.net

Preparation:

To use the script. You'll need:

• TensorFlow >= 1.0.0 (>=1.1 for MultiGPU)

• OpenCV bindings for Python

• 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 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},
}