Parital FC

Pytorch is currently still a preview version. There is a 5 thousandth difference between the sampling of 0.1 and the paper. **If you want to reproduce the accuracy in the paper, it is strongly recommended to use mxnet first. ** All experiments in the paper are done by mxnet.

Pytorch 目前是还是预览版本，模型并行是没问题的，但是0.1的采样暂时无法使用，
如果要使用采样(复现论文中的精度)，强烈建议优先使用mxnet, 所有论文的实验均是mxnet完成的。
我们会马上修复这个bug。

Insightface 社区需要大家一起贡献才会变得更好，欢迎大家提交Pull Request.

TODO

-[ ] No BUG Sampling -[ ] Mixed precision training -[ ] Pipeline Parallel -[ ] Checkpoint -[ ] Docker -[ ] A Wonderful Documents

How to run

cuda=10.1
pytorch==1.6.0
pip install -r requirement.txt

bash run.sh

使用 bash run.sh 这个命令运行。

Results

MS1MV2-IJBC

+--------------+-------+-------+--------+-------+-------+-------+
|   Methods    | 1e-06 | 1e-05 | 0.0001 | 0.001 |  0.01 |  0.1  |
+--------------+-------+-------+--------+-------+-------+-------+
| cosface-IJBC | 86.63 | 94.22 | 96.37  | 97.61 | 98.34 | 99.08 |
+--------------+-------+-------+--------+-------+-------+-------+

Citation

If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:

Partial FC

@inproceedings{an2020partical_fc,
  title={Partial FC: Training 10 Million Identities on a Single Machine},
  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
  Zhang, Debing and Fu Ying},
  booktitle={Arxiv 2010.05222},
  year={2020}
}

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