Incremental-Network-Quantization

Caffe Implementation for Incremental network quantization, we modify the original caffe, the Installation is follow caffe.

the default source code is 5 bits weights-only quantization, you can by changing parameter "partition"(/src/caffe/blob.cpp) to control the quantization step.

INQ usage

0.you must be farmilar with caffe training imagenet tutorial

1.Train 5 bits Alexnet with Imagenet:

python run.py

Please download float-point ImageNet-pre-trained AlexNet/VGG models and power-of-two model manually from BaiduYun, and put it into $/models/bvlc_alexnet/.

2.At continuous partition steps, the output logs are saved as run1_log.out, run2_log.out, run3_log.out,..., respectively

Citing INQ

If you find INQ useful in your research, please consider citing:

@inproceedings{zhou2017,
title={Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights},
author={Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen},
booktitle={International Conference on Learning Representations,ICLR2017},
year={2017},
}

Tips:

1. Real-time data shuffling is useful