Cross-Modal-Re-ID-baseline (AGW)

Pytorch Code of AGW method [1] for Cross-Modality Person Re-Identification (Visible Thermal Re-ID) on RegDB dataset [3] and SYSU-MM01 dataset [4].

We adopt the two-stream network structure introduced in [2]. ResNet50 is adopted as the backbone. The softmax loss is adopted as the baseline.

Datasets	Pretrained	Rank@1	mAP	mINP	Model
#RegDB	ImageNet	~ 70.05%	~ 66.37%	~50.19%	-----
#SYSU-MM01	ImageNet	~ 47.50%	~ 47.65%	~35.30%	GoogleDrive

*Both of these two datasets may have some fluctuation due to random spliting. The results might be better by finetuning the hyper-parameters.

1. Prepare the datasets.

• (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 (mangye16@gmail.com).

• (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.

2. Training.

Train a model by

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

• --dataset: which dataset "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/.

3. Testing.

Test a model on SYSU-MM01 or RegDB dataset by

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

• --dataset: which dataset "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.

4. Citation

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

@article{arxiv20reidsurvey,
  title={Deep Learning for Person Re-identification: A Survey and Outlook},
  author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H.},
  journal={arXiv preprint arXiv:2001.04193},
  year={2020},
}

5. References.

[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.

Contact: mangye16@gmail.com