Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning
Convert the dataset format (like Market1501).
python prepare_sysu.py # for SYSU-MM01
python prepare_regdb.py # for RegDB
You need to change the file path in the prepare_sysu(regdb).py
.
Note: a pre-processed dataset can be downloaded from Baidu Netdisk (Password: ReID) or Google Drive.
./train_sysu.sh # for SYSU-MM01
./train_regdb.sh # for RegDB
Two training stages are included and you need to specify the training stage by commenting another stage's main_worker
like this:
main_worker_stage1(args,log_s1_name) # Stage 1
# main_worker_stage2(args,log_s1_name,log_s2_name) # Stage 2
Update: We optimized the code to make the training more stable. In the 2nd stage of training, we recommend setting use_hard
to True
, referring to [1].
./test_sysu.sh # for SYSU-MM01
./test_regdb.sh # for RegDB
@InProceedings{Wu_2023_CVPR,
author = {Wu, Zesen and Ye, Mang},
title = {Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {9548-9558}
}
Our trained models can be downloaded here.
[1] Dai, Zuozhuo, et al. "Cluster contrast for unsupervised person re-identification." Proceedings of the Asian conference on computer vision. 2022.
The code is implemented based on ClusterContrast and ADCA.