In this repo, we include the 1st Place submission to AICity Challenge 2020 re-id track (Baidu-UTS submission) [Paper]
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 training code at Here.
- We include the Pytorch training code at Here.
AICITY2020 Challange Track2 Leaderboard
TeamName | mAP | Link |
---|---|---|
Baidu-UTS(Ours) | 84.1% | code |
RuiYanAI | 78.1% | code |
DMT | 73.1% | code |
I have updated the feature. You may download from GoogleDrive or OneDrive
├── 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
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[Vehicle re-ID Paper Collection] https://github.com/layumi/Vehicle_reID-Collection
-
[Person re-ID Baseline] https://github.com/layumi/Person_reID_baseline_pytorch
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[Person/Vehicle Generation] https://github.com/NVlabs/DG-Net
Please cite this paper if it helps your research:
@article{zheng2020beyond,
title={Going beyond real data: a robust visual representation for vehicle re-identification},
author={Zhedong Zheng, Minyue Jiang, Zhigang Wang, Jian Wang, Zechen Bai, Xuanmeng Zhang, Xin Yu, Xiao Tan, Yi Yang, Shilei Wen, and Errui Ding},
journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
year={2020}
}