DoReFa-Net

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 those runtime bit-op libraries for now. In these examples, bit operations are run in float32.

Pretrained model for 1-2-6-AlexNet is available at google drive. It's provided in the format of numpy dictionary, so it should be very easy to port into other applications.

Preparation:

To use the script. You'll need:

• TensorFlow >= 0.8

• OpenCV bindings for Python

• tensorpack:

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

• Look at the docstring in svhn-digit-dorefa.py or alexnet-dorefa.py to see detailed usage and performance.

Support

Please use github issues for any issues related to the code. 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},
}