Pytorch implementation for MMD-ReID: A Simple but Effective solution for Visible-Thermal Person ReID. Accepted at BMVC 2021 (Oral)
Paper link: https://arxiv.org/abs/2111.05059
Github Code: https://github.com/vcl-iisc/MMD-ReID
Presentation Slides: https://drive.google.com/file/d/1S0sfA7PMyzqGPnG5izGBeZ7uClsJ1uA3/view?usp=sharing
Project webpage: https://vcl-iisc.github.io/mmd-reid-web/
Recorded Talk: https://recorder-v3.slideslive.com/?share=55344&s=d3b53e98-4362-410a-825d-77706f8b71c4
- Python 3.7
- GPU memory ~ 10G
- NumPy 1.19
- PyTorch 1.8
Our code extends the pytorch implementation of Parameter Sharing Exploration and Hetero center triplet loss for VT Re-ID in Github. Please refer to the offical repo for details of data preparation.
python train_mine.py --dataset sysu --gpu 1 --pcb off --share_net 3 --batch-size 4 --num_pos 4 --dist_disc 'margin_mmd' --margin_mmd 1.40 --run_name 'margin_mmd1.40'
python test.py --dataset sysu --gpu 0 --pcb off --share_net 3 --batch-size 4 --num_pos 4 --run_name 'margin_mmd1.40'
| Rank@1 | Rank@10 | Rank@20 | mAP | |
|---|---|---|---|---|
| SYSU-MM01 (All search Single shot) | 66.75% | 94.16% | 97.38% | 62.25% |
| RegDB (Visible to Thermal) | 95.06% | 98.67% | 99.31% | 88.95% |
If you use this code, please cite our work as:
@inproceedings{jambigi2021mmd,
title={MMD-ReID: A Simple but Effective solution for Visible-Thermal Person ReID},
author={Jambigi, Chaitra and Rawal, Ruchit and Chakraborty, Anirban},
booktitle={British Machine Vision Conference},
year={2021}
}