This project is the PyTorch implementation of our paper : IR^2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks. Trained Models of IR2Net-A/B/C are available, the link in the file "Trained Models".
Dependencies
- Python 3.7
- Pytorch == 1.3.0
Accuracy
CIFAR-10:
| Model | Bit-Width (W/A) | Accuracy (%) |
|---|---|---|
| ResNet-20 | 1 / 1 | 87.2 |
| VGG-Small | 1 / 1 | 91.5 |
| ResNet-18 | 1 / 1 | 92.5 |
ImageNet:
| Method | Bit-Width (W/A) | Top-1 (%) | Top-5 (%) |
|---|---|---|---|
| IR2Net-A | 1 / 1 | 68.2 | 88.0 |
| IR2Net-B | 1 / 1 | 67.0 | 87.1 |
| IR2Net-C | 1 / 1 | 66.6 | 87.0 |
| IR2Net-D | 1 / 1 | 63.8 | 85.5 |
Code Reference
[1] H. Qin, R. Gong, X. Liu, M. Shen, Z. Wei, F. Yu, and J. Song, “Forward and backward information retention for accurate binary neural networks,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 2247–2256.
[2] Z. Liu, Z. Shen, M. Savvides, and K. Cheng, “Reactnet: Towards precise binary neural network with generalized activation functions,” in Proc. European Conference on Computer Vision, 2020, pp. 143–159.
[3] K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, “Ghostnet: More features from cheap operations,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2020, pp. 1577–1586.
If you find our code useful for your research, please consider citing:
@article{xue2023IR^2Net,
title={IR^2Net: information restriction and information recovery for accurate binary neural networks},
DOI={10.1007/s00521-023-08495-z},
author={Xue, Ping and Lu, Yang and Chang, Jingfei and Wei, Xing and Wei, Zhen},
journal={Neural Computing and Applications},
year={2023},
month={Mar}
}