[CVPR2023] PHA: Patch-wise High-frequency Augmentation for Transformer-based Person Re-identification [pdf]
Official Code for the CVPR 2023 paper [PHA: Patch-wise High-frequency Augmentation for Transformer-based Person Re-identification].
pip install -r requirements.txt
(we use 32G V100 for training and evaluation.)You need to download the ImageNet pretrained transformer model : ViT-Base,
We utilize 1 GPU for training.
CUDA_VISIBLE_DEVICES=0 python train.py --config_file configs/Cuhk03_labeled/vit_transreid_stride.yml
CUDA_VISIBLE_DEVICES=0 python train.py --config_file configs/Market/vit_transreid_stride.yml
CUDA_VISIBLE_DEVICES=0 python train.py --config_file configs/MSMT17/vit_transreid_stride.ymlIf you find this code useful for your research, please cite our paper
@InProceedings{Zhang_2023_CVPR,
author = {Guiwei Zhang, Yongfei Zhang, Tianyu Zhang, Bo Li1, Shiliang Pu},
title = {PHA: Patch-wise High-frequency Augmentation for Transformer-based Person Re-identification},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {14133-14142}
}
Our code is based on TransReID. Thanks for the great work!
@InProceedings{He_2021_ICCV,
author = {He, Shuting and Luo, Hao and Wang, Pichao and Wang, Fan and Li, Hao and Jiang, Wei},
title = {TransReID: Transformer-Based Object Re-Identification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {15013-15022}
}