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https://github.com/layumi/aicity-reid-2020
:red_car: The 1st Place Submission to AICity Challenge 2020 re-id track (Baidu-UTS submission)
https://github.com/layumi/aicity-reid-2020
aicity cityflow cvpr2020 paddlepaddle pytorch vehicle vehicle-reid veri-776
Last synced: 13 days ago
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:red_car: The 1st Place Submission to AICity Challenge 2020 re-id track (Baidu-UTS submission)
- Host: GitHub
- URL: https://github.com/layumi/aicity-reid-2020
- Owner: layumi
- License: mit
- Created: 2020-04-21T08:47:52.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-09-06T10:57:15.000Z (about 3 years ago)
- Last Synced: 2024-08-01T22:37:44.953Z (3 months ago)
- Topics: aicity, cityflow, cvpr2020, paddlepaddle, pytorch, vehicle, vehicle-reid, veri-776
- Language: Python
- Homepage:
- Size: 8.76 MB
- Stars: 450
- Watchers: 15
- Forks: 110
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## AICity-reID 2020 (track2)
In this repo, we include the 1st Place submission to [AICity Challenge](https://www.aicitychallenge.org/) 2020 re-id track (Baidu-UTS submission)
[[Paper]](https://github.com/layumi/AICIty-reID-2020/blob/master/paper.pdf) [[Video]](https://www.bilibili.com/video/BV1hK411A78n/)
We fuse the models trained on Paddlepaddle and Pytorch. To illustrate them, we provide the two training parts seperatively as following.
- We include the [Paddlepaddle](https://github.com/PaddlePaddle/Paddle) training code at [Here](https://github.com/PaddlePaddle/Research/tree/master/CV/PaddleReid).
- We include the [Pytorch](https://pytorch.org/) training code at [Here](https://github.com/layumi/AICIty-reID-2020/tree/master/pytorch).
### Performance：
AICITY2020 Challange Track2 Leaderboard
|TeamName|mAP|Link|
|--------|----|-------|
|**Baidu-UTS(Ours)**|84.1%|[code](https://github.com/layumi/AICIty-reID-2020)|
|RuiYanAI|78.1%|[code](https://github.com/Xiangyu-CAS/AICity2020-VOC-ReID)|
|DMT|73.1%|[code](https://github.com/heshuting555/AICITY2020_DMT_VehicleReID)|
### Trained Models
How to extract features? Please refer to [[Here]](https://github.com/layumi/AICIty-reID-2020/tree/master/pytorch#extract-feature-for-post-processing)
and there is one simplified version at [[Here]](https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial#part-21-extracting-feature-python-testpy).
Here we provide one model of the final models.
- SE_imbalance_s1_384_p0.5_lr2_mt_d0_b24+v+aug (AICity 2020) can be downloaded at [[GoogleDrive]](https://drive.google.com/file/d/1AZ4hHbRbz2T8OHJ6QTG9bR7CP2zUOyQh/view?usp=sharing).
The state-of-the-art model achieving 83.41% mAP on [VeRi-776](https://github.com/JDAI-CV/VeRidataset), which is based on our TMM paper.
- Training on VehicelNet only (80.91): Res50_imbalance_s1_256_p0.5_lr2_mt_d0_b48 (TMM) can be downloaded at [[GoogleDrive]](https://drive.google.com/file/d/1wUbYm5-EJs0W-LAGS69yvb33D6NkFWpH/view?usp=sharing).
- Finetuning on VeRi (83.41): ft_Res50_imbalance_s1_256_p0.5_lr1_mt_d0.2_b48_w5 (TMM) can be downloaded at [[GoogleDrive]](https://drive.google.com/file/d/1Sor7Grh_1Kot6CBLaw2alDT4Nr3JuH3C/view?usp=sharing).
### Extracted Features & Camera Prediction & Direction Prediction:
I have updated the feature. You may download from [GoogleDrive](https://drive.google.com/file/d/1q0ap5smXoRIQ-oEUMbSMMSl_lEOT0Fk6/view?usp=sharing) or [OneDrive](https://studentutsedu-my.sharepoint.com/:u:/g/personal/12639605_student_uts_edu_au/EdxlWLP9bB9Bga0jfDyoIO8Berahz8plAeRY6M4t8g_6iA?e=mSttQx) (expired by July 1 2022)
```
├── final_features/
│ ├── features/ /* extracted pytorch feature
│ ├── pkl_feas/ /* extracted paddle feature (include direction similarity)
│ ├── real_query_fea_ResNeXt101_32x8d_wsl_416_416_final.pkl
| ...
│ ├── query_fea_Res2Net101_vd_final2.pkl
│ ├── gallery_cam_preds_baidu.txt /* gallery camera prediction
│ ├── query_cam_preds_baidu.txt /* query camera prediction
| ├── submit_cam.mat /* camera feature for camera similarity calculation
```
### Related Repos：
- :helicopter: Drone-based building re-id [[code]](https://github.com/layumi/University1652-Baseline) [[paper]](https://arxiv.org/abs/2002.12186)
- [Vehicle re-ID Paper Collection] https://github.com/layumi/Vehicle_reID-Collection
- [Person re-ID Baseline] https://github.com/layumi/Person_reID_baseline_pytorch
- [Person/Vehicle Generation] https://github.com/NVlabs/DG-Net
### Citation
Please cite this paper if it helps your research:
```bibtex
@inproceedings{zheng2020going,
title={Going beyond real data: A robust visual representation for vehicle re-identification},
author={Zheng, Zhedong and Jiang, Minyue and Wang, Zhigang and Wang, Jian and Bai, Zechen and Zhang, Xuanmeng and Yu, Xin and Tan, Xiao and Yang, Yi and Wen, Shilei and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
pages={598--599},
year={2020}
}
@article{zheng2020beyond,
title={VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification},
author={Zheng, Zhedong and Ruan, Tao and Wei, Yunchao and Yang, Yi and Mei, Tao},
journal={IEEE Transactions on Multimedia (TMM)},
doi={10.1109/TMM.2020.3014488},
note={\mbox{doi}:\url{10.1109/TMM.2020.3014488}},
year={2020}
}
```
The heatmap visualization is based on
```bibtex
@article{zheng2017discriminatively,
title={A discriminatively learned cnn embedding for person reidentification},
author={Zheng, Zhedong and Zheng, Liang and Yang, Yi},
journal={ACM transactions on multimedia computing, communications, and applications (TOMM)},
volume={14},
number={1},
pages={1--20},
year={2017},
publisher={ACM New York, NY, USA}
}
```