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This is the official PyTorch implementation of the ICCV 2023 paper.

UniFace: Unified Cross-Entropy Loss for Deep Face Recognition.pdf

Supplementary.pdf

1. Prepare dataset

   Download CASIA-Webface preprocessed by insightface.

   unzip faces_webface_112x112.zip

2. Train model

   Modify the 'data_path' in train.py (Line 56)

   Select and uncomment the 'loss' in backbone.py (Line 67)

   python train.py

3. Test model

   python pytorch2onnx.py
   zip model.zip model.onnx

   Upload model.zip to MFR Ongoing and then wait for the results.

   We provide a pre-trained model (ResNet-50) on Google Drive for easy and direct development. This model is trained on CASIA-WebFace and achieved 48.42% on MR-All and 99.56% on LFW.

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

@InProceedings{Zhou_2023_ICCV,
  author    = {Zhou, Jiancan and Jia, Xi and Li, Qiufu and Shen, Linlin and Duan, Jinming},
  title     = {UniFace: Unified Cross-Entropy Loss for Deep Face Recognition},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2023},
  pages     = {20730-20739}
}