layumi / Aicity Reid 2020

AICity-reID 2020 (track2)

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.

Performance：

AICITY2020 Challange Track2 Leaderboard

Extracted Features & Camera Prediction & Direction Prediction:

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

Related Repos：

• 🚁 Drone-based building re-id [code] [paper]

• [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:

@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},
  year={2020}
}