The code of VarGFaceNet is released under the MIT License. There is no limitation for both acadmic and commercial usage.
This is a MXNET implementation of VarGFaceNet. We achieved 1st place at Light-weight Face Recognition challenge/workshop on ICCV 2019(deepglint-light track)LFR2019
For details, please read the following papers:
- VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
- VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
- train from scratch:
| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) | deepglint-light(%,TPR@FPR=1e-8) |
|---|---|---|---|---|
| Ours | 0.99683 | 0.98086 | 0.98100 | 0.855 |
- recursive knowledge distillation:
| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) | deepglint-light(%,TPR@FPR=1e-8) |
|---|---|---|---|---|
| recursive=1 | 0.99783 | 0.98400 | 0.98067 | 0.88334 |
| recursive=2 | 0.99833 | 0.98271 | 0.98050 | 0.88784 |
If you find VarGFaceNet useful in your research, please consider to cite the following related papers:
@article{vargfacenet,
author = {Yan, Mengjia and Zhao, Mengao and Xu, Zining and Zhang, Qian and Wang, Guoli and Su, Zhizhong},
title = {VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition},
journal = {In Proceedings of the IEEE International Conference on Computer Vision Workshops},
year = 2019
}
@article{zhang2019vargnet,
title={VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing},
author={Zhang, Qian and Li, Jianjun and Yao, Meng and Song, Liangchen and Zhou, Helong and Li, Zhichao and Meng, Wenming and Zhang, Xuezhi and Wang, Guoli},
journal={arXiv preprint arXiv:1907.05653},
year={2019}
}
[Mengao Zhao](mengao.zhao[at]gmail.com)
[Mengjia Yan](mengjyan[at]gmail.com)