Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang
This repository is the official PyTorch implementation of paper CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition. (The work has been accepted by CVPR2020)
- torch == 1.1.0
- torchvision == 0.3.0
- tensorboardX == 1.7
- bcolz == 1.2.1
- Python 3
# To train the model:
sh train.sh
# To evaluate the model:
(1)please first download the val data in https://github.com/ZhaoJ9014/face.evoLVe.PyTorch.
(2)set the checkpoint dir in config.py
sh evaluate.sh
You can change the experimental setting by simply modifying the parameter in the config.py
The IR101 pretrained model can be downloaded here. [Baidu Cloud](link: https://pan.baidu.com/s/1bu-uocgSyFHf5pOPShhTyA passwd: 5qa0), Google Drive
If you find this code useful in your research, please consider citing us:
@article{huang2020curricularface,
title={CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition},
author={Yuge Huang and Yuhan Wang and Ying Tai and Xiaoming Liu and Pengcheng Shen and Shaoxin Li and Jilin Li, Feiyue Huang},
booktitle={CVPR},
pages={1--8},
year={2020}
}
If you have any questions about our work, please do not hesitate to contact us by emails. Yuge Huang: [email protected] Ying Tai: [email protected]