README.md

LQ-Nets

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

Microsoft Research Asia (MSRA).

Introduction

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

Dependencies

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

Usage

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.

Results

ImageNet Experiments

Quantizing both weight and activation

Model	Bit-width(W/A)	Top-1(%)	Top-5(%)
ResNet-18	1/2	62.6	84.3
ResNet-18	2/2	64.9	85.9
ResNet-18	3/3	68.2	87.9
ResNet-18	4/4	69.3	88.8
ResNet-34	1/2	66.6	86.9
ResNet-34	2/2	69.8	89.1
ResNet-34	3/3	71.9	90.2
ResNet-50	1/2	68.7	88.4
ResNet-50	2/2	71.5	90.3
ResNet-50	3/3	74.2	91.6
ResNet-50	4/4	75.1	92.4
AlexNet	1/2	55.7	78.8
AlexNet	2/2	57.4	80.1
DenseNet-121	2/2	69.6	89.1
VGG-Variant	1/2	67.1	87.6
VGG-Variant	2/2	68.8	88.6
GoogLeNet-Variant	1/2	65.6	86.4
GoogLeNet-Variant	2/2	68.2	88.1

Quantizing weight only

Model	Bit-width(W/A)	Top-1(%)	Top-5(%)
ResNet-18	2/32	68.0	88.0
ResNet-18	3/32	69.3	88.8
ResNet-18	4/32	70.0	89.1
ResNet-50	2/32	75.1	92.3
ResNet-50	4/32	76.4	93.1
AlexNet	2/32	60.5	82.7

More results can be found in the paper.

Citation

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}
}