# Cross-Modal-Re-ID-baseline

**Repository Path**: Cloud-Rambler/Cross-Modal-Re-ID-baseline

## Basic Information

- **Project Name**: Cross-Modal-Re-ID-baseline
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-09-09
- **Last Updated**: 2022-04-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://drive.google.com/open?id=181K9PQGnej0K5xNX9DRBDPAf3K9JosYk)|

*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](http://dm.dongguk.edu/link.html) 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](http://isee.sysu.edu.cn/project/RGBIRReID.htm).

   - 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
  ```bash
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 
  ```bash
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