Introduction
DCQ (Dynamic Class Queue) is a state-of-the-art face recognition method for training million-IDs datasets.
This repo is the official implementation for CVPR 2021 paper: Dynamic Class Queue for Large Scale Face Recognition In the Wild. [paper]
News
!!! Research Intern Wanted. Send CV to libi01[AT]baidu.com.2021-08-14: We are the champion of CSIG FAT-AI 2021 masked face recognition challenge. Details
2021-08-03: Initial code release.
Quick Start
Prerequisite
Install PaddlePaddle 2.1
Download the MS1MV2 dataset and common test benchmarks via BaiduYun
url: https://pan.baidu.com/s/1PYY3h-jEVURWwQYvLzE3Mg password: m2m8
# untar file
cat xaa xab xac xad xae | tar xf -Training
# Train iresnet50
bash train_scripts/train_dcq_ir50_q8192_ms1mv2.sh
# Train iresnet100
bash train_scripts/train_dcq_ir100_q8192_ms1mv2.shEvaluation
data_root=./DCQ_train_test_data/common_test_benchmarks
model=Logs/dcq_ires50_q8192_ms1mv2
epoch=19
python eval/eval_verification.py $model $epoch --save-prefix lfw --filelist '$data_root/lfw.filelist' --label-path '$data_root/lfw_label.npy'
python eval/eval_verification.py $model $epoch --save-prefix cplfw --filelist '$data_root/cplfw.filelist' --label-path '$data_root/cplfw_label.npy'
python eval/eval_verification.py $model $epoch --save-prefix agedb_30 --filelist '$data_root/agedb_30.filelist' --label-path '$data_root/agedb_30_label.npy'Contributing
Main contributors:
- Bi Li
- Jianwei Li
- Nan Peng
Credit
This code is largely based on moco and face.evoLVe.
Citation
@InProceedings{Li_2021_CVPR,
author = {Li, Bi and Xi, Teng and Zhang, Gang and Feng, Haocheng and Han, Junyu and Liu, Jingtuo and Ding, Errui and Liu, Wenyu},
title = {Dynamic Class Queue for Large Scale Face Recognition in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {3763-3772}
}