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https://github.com/mangye16/Cross-Modal-Re-ID-baseline
Pytorch Code for Cross-Modality Person Re-Identification (Visible Thermal/Infrared Re-ID)
https://github.com/mangye16/Cross-Modal-Re-ID-baseline
Last synced: 3 months ago
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Pytorch Code for Cross-Modality Person Re-Identification (Visible Thermal/Infrared Re-ID)
- Host: GitHub
- URL: https://github.com/mangye16/Cross-Modal-Re-ID-baseline
- Owner: mangye16
- License: mit
- Created: 2018-12-10T20:26:14.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-11-30T13:19:34.000Z (12 months ago)
- Last Synced: 2024-05-11T13:30:59.919Z (6 months ago)
- Language: Python
- Homepage:
- Size: 320 KB
- Stars: 328
- Watchers: 10
- Forks: 72
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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 ([email protected]).
- (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: [email protected]