Awesome

AICity-reID 2020 (track2)

In this repo, we include the 1st Place submission to AICity Challenge 2020 re-id track (Baidu-UTS submission)

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

TeamName	mAP	Link
Baidu-UTS(Ours)	84.1%	code
RuiYanAI	78.1%	code
DMT	73.1%	code

Trained Models

How to extract features? Please refer to [Here] and there is one simplified version at [Here]. 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].

The state-of-the-art model achieving 83.41% mAP on VeRi-776, 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].
Finetuning on VeRi (83.41): ft_Res50_imbalance_s1_256_p0.5_lr1_mt_d0.2_b48_w5 (TMM) can be downloaded at [GoogleDrive].

Extracted Features & Camera Prediction & Direction Prediction:

I have updated the feature. You may download from GoogleDrive or OneDrive (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] [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},
  note={\mbox{doi}:\url{10.1109/TMM.2020.3014488}},
  year={2020}
}

The heatmap visualization is based on

@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}
}