By Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, Gang Hua.

Microsoft Research Asia (MSRA).

This repository contains the training code of LQ-Nets introduced in our ECCV 2018 paper:

D. Zhang*, J. Yang*, D. Ye* and G. Hua. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks. ECCV 2018 (*: Equal contribution) PDF

• Python 2.7 or 3.3+
• Python bindings for OpenCV
• TensorFlow >= 1.3.0
• TensorPack

Download the ImageNet dataset and decompress into the structure like

dir/
  train/
    n01440764/
      n01440764_10026.JPEG
      ...
    ...
  val/
    ILSVRC2012_val_00000001.JPEG
    ...

To train a quantized "pre-activation" ResNet-18, simply run

python imagenet.py --gpu 0,1,2,3 --data /PATH/TO/IMAGENET --mode preact --depth 18 --qw 1 --qa 2 --logdir_id w1a2 

After the training, the result model will be stored in ./train_log/w1a2.

For more options, please refer to python imagenet.py -h.

ImageNet Experiments

Quantizing both weight and activation

Quantizing weight only

More results can be found in the paper.

If you use our code or models in your research, please cite our paper with

@inproceedings{ZhangYangYeECCV2018,
    author = {Zhang, Dongqing and Yang, Jiaolong and Ye, Dongqiangzi and Hua, Gang},
    title = {LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2018}
}