TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective (ICCV-2023)

This is the official PyTorch implementation of TransFace.

Arxiv

ModelScope

You can quickly experience and invoke our TransFace model on the ModelScope.

Requirements

• Install Pytorch (torch>=1.9.0)
• pip install -r requirement.txt

Datasets

You can download the training datasets, including MS1MV2 and Glint360K:

• MS1MV2: Google Drive
• Glint360K: Baidu (code=:o3az)

You can download the test dataset IJB-C as follows:

• IJB-C: Google Drive

How to Train Models

1. You need to modify the path of training data in every configuration file in folder configs.

2. To run on a machine with 8 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py 

How to Test Models

1. You need to modify the path of IJB-C dataset in eval_ijbc.py.

2. Run:

python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_s/model.pt --result-dir work_dirs/glint360k_vit_s --network vit_s_dp005_mask_0 > ijbc_glint360k_vit_s.log 2>&1 &

TransFace Pretrained Models

You can download the TransFace models reported in our paper as follows:

Training Data	Model	IJB-C(1e-6)	IJB-C(1e-5)	IJB-C(1e-4)	IJB-C(1e-3)	IJB-C(1e-2)	IJB-C(1e-1)
MS1MV2	TransFace-S	86.75	93.87	96.45	97.51	98.34	98.99
MS1MV2	TransFace-B	86.73	94.15	96.55	97.73	98.47	99.11
MS1MV2	TransFace-L	86.90	94.55	96.59	97.80	98.45	99.04

Training Data	Model	IJB-C(1e-6)	IJB-C(1e-5)	IJB-C(1e-4)	IJB-C(1e-3)	IJB-C(1e-2)	IJB-C(1e-1)
Glint360K	TransFace-S	89.93	96.06	97.33	98.00	98.49	99.11
Glint360K	TransFace-B	88.64	96.18	97.45	98.17	98.66	99.23
Glint360K	TransFace-L	89.71	96.29	97.61	98.26	98.64	99.19

You can test the accuracy of these model: (e.g. Glint360K TransFace-L)

python eval_ijbc.py --model-prefix work_dirs/glint360k_vit_l/glint360k_model_TransFace_L.pt --result-dir work_dirs/glint360k_vit_l --network vit_l_dp005_mask_005 > ijbc_glint360k_vit_l.log 2>&1 &

Citation

• If you find it helpful for you, please cite our paper

@inproceedings{dan2023transface,
  title={TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective},
  author={Dan, Jun and Liu, Yang and Xie, Haoyu and Deng, Jiankang and Xie, Haoran and Xie, Xuansong and Sun, Baigui},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={20642--20653},
  year={2023}
}

Acknowledgments

We thank Insighface for the excellent code base.