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Change the data root in pretrain_cuhk.py, then run:
python train_pretrain.py
Training with text-based re-ID datasets
# We leverage four V-100 GPUs for training on CUHK-PEDES and ICFG-PEDES datasets.# training with multi-gpus
sh start.sh
# or, you could also train them with a single gpu, but slow speed, maybe better performance.
python train_cuhkpedes_gpu.py
python train_icfg_gpu.py
# As RSTPReid is small, we leverage only one V-100 GPU for training.
python train_rstp.py
You can obtain the datasets from corresponding authors. We provide our processed json files at Baidu Pan[xktc].
Citations
If you find our work helpful, please cite using this BibTeX:
@inproceedings{shu2023see,
title={See finer, see more: Implicit modality alignment for text-based person retrieval},
author={Shu, Xiujun and Wen, Wei and Wu, Haoqian and Chen, Keyu and Song, Yiran and Qiao, Ruizhi and Ren, Bo and Wang, Xiao},
booktitle={ Proceedings of the European conference on computer vision Workshops (ECCVW)},
pages={624--641},
year={2023},
organization={Springer}
}
Contact us
If you have any questions, comments or suggestions, please do not hesitate to contact us at shuxj@mail.ioa.ac.cn.
About
Code for ECCV 2022 Workshop paper "See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval"