/ReBNN

Primary LanguagePython

Resilient Binary Neural Network (ReBNN)

Pytorch implementation of our paper "Resilient Binary Neural Network" accepted by AAAI2023 as oral presentation.

Tips

Any problem, please contact the first author (Email: shengxu@buaa.edu.cn).

Our code is heavily borrowed from ReActNet (https://github.com/liuzechun/ReActNet).

Dependencies

  • Python 3.8
  • Pytorch 1.7.1
  • Torchvision 0.8.2

ReBNN with two-stage tranining

We test our ReBNN using the same ResNet-18 structure and training setttings as ReActNet, and obtain 66.9% top-1 accuracy.

Methods Top-1 acc Top-5 acc Quantized model link Log
ReActNet 65.9 - Model -
ReCU 66.4 86.5 Model -
RBONN 66.7 87.0 Model -
ReBNN 66.9 87.1 - -

To verify the performance of our quantized models with ReActNet-like structure on ImageNet, please do as the following steps:

  1. Finish the first stage training using ReActNet.
  2. Use the following command:
cd 2_step2_rebnn 
bash run.sh

If you find this work useful in your research, please consider to cite:

@inproceedings{xu2023resilient,
  title={Resilient Binary Neural Network},
  author={Xu, Sheng and Li, Yanjing and Ma, Teli and Lin, Mingbao and Dong, Hao and Zhang, Baochang and Gao, Peng and Lu, Jinhu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={9},
  pages={10620--10628},
  year={2023}
}