Pytorch Code of AGW method [1] for Cross-Modality Person Re-Identification (Visible Thermal Re-ID) on RegDB dataset [3], SYSU-MM01 dataset [4] and LLCM dataset [5]. This code is based on mangye16 [1, 2].

We adopt the two-stream network structure introduced in [2]. ResNet50 is adopted as the backbone. The softmax loss is adopted as the baseline. Both of these three datasets may have some fluctuation due to random spliting. The results might be better by finetuning the hyper-parameters.

• (1) RegDB Dataset [3]: The RegDB dataset can be downloaded from this website by submitting a copyright form.

  • (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

  • A private download link can be requested via sending me an email ([email protected]).

• (2) SYSU-MM01 Dataset [4]: The SYSU-MM01 dataset can be downloaded from this website.

  • run python pre_process_sysu.py to pepare the dataset, the training data will be stored in ".npy" format.

• (3) LLCM Dataset [5]: The LLCM dataset can be downloaded by sending a signed dataset release agreement copy to [email protected].

Train a model by

python train.py --dataset llcm --lr 0.1 --method agw --gpu 1

• --dataset: which dataset "llcm", "sysu" or "regdb".

• --lr: initial learning rate.

• --method: method to run or baseline.

• --gpu: which gpu to run.

You may need mannully define the data path first.

Parameters: More parameters can be found in the script.

Sampling Strategy: N (= bacth size) person identities are randomly sampled at each step, then randomly select four visible and four thermal image. Details can be found in Line 302-307 in train.py.

Training Log: The training log will be saved in log/" dataset_name"+ log. Model will be saved in save_model/.

Test a model on LLCM, SYSU-MM01 or RegDB dataset by

python test.py --mode all --resume 'model_path' --gpu 1 --dataset llcm

• --dataset: which dataset "llcm", "sysu" or "regdb".

• --mode: "all" or "indoor" all search or indoor search (only for sysu dataset).

• --trial: testing trial (only for RegDB dataset).

• --resume: the saved model path.

• --gpu: which gpu to run.

Please kindly cite this paper in your publications if it helps your research:

@article{zhang2023diverse,
  title={Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification},
  author={Zhang, Yukang and Wang, Hanzi},
  journal={arXiv preprint arXiv:2303.14481},
  year={2023}
}

[1] M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C., Hoi. Deep learning for person re-identification: A survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.

[2] M. Ye, X. Lan, Z. Wang, and P. C. Yuen. Bi-directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification. IEEE Transactions on Information Forensics and Security (TIFS), 2019.

[3] D. T. Nguyen, H. G. Hong, K. W. Kim, and K. R. Park. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors, 17(3):605, 2017.

[4] A. Wu, W.-s. Zheng, H.-X. Yu, S. Gong, and J. Lai. Rgb-infrared crossmodality person re-identification. In IEEE International Conference on Computer Vision (ICCV), pages 5380–5389, 2017.

[5] Zhang Y, Wang H. Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification[J]. arXiv preprint arXiv:2303.14481, 2023.

If you have any question, please feel free to contact us. [email protected].