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AICITY2021_Track2_DMT

The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

Introduction

Detailed information of NVIDIA AI City Challenge 2021 can be found here.

The code is modified from AICITY2020_DMT_VehicleReID, TransReID and reid_strong baseline.

Get Started

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/michuanhaohao/AICITY2021_Track2_DMT.git

3. Install dependencies: pip install requirements.txt

   We use cuda 11.0/python 3.7/torch 1.6.0/torchvision 0.7.0 for training and testing.

4. Prepare Datasets Download Original dataset, Cropped_dataset, and SPGAN_dataset.

├── AIC21/
│   ├── AIC21_Track2_ReID/
│   	├── image_train/
│   	├── image_test/
│   	├── image_query/
│   	├── train_label.xml
│   	├── ...
│   	├── training_part_seg/
│   	    ├── cropped_patch/
│   	├── cropped_aic_test
│   	    ├── image_test/
│   	    ├── image_query/		
│   ├── AIC21_Track2_ReID_Simulation/
│   	├── sys_image_train/
│   	├── sys_image_train_tr/

5. Put pre-trained models into ./pretrained/
   • resnet101_ibn_a-59ea0ac6.pth, densenet169_ibn_a-9f32c161.pth, resnext101_ibn_a-6ace051d.pth and se_resnet101_ibn_a-fabed4e2.pth can be downloaded from IBN-Net
   • resnest101-22405ba7.pth can be downloaded from ResNest
   • jx_vit_base_p16_224-80ecf9dd.pth can be downloaded from here

Trainint and Test

We utilize 1 GPU (32GB) for training. You can train and test one backbone as follow.

# ResNext101-IBN-a
python train.py --config_file configs/stage1/resnext101a_384.yml MODEL.DEVICE_ID "('0')"
python train_stage2_v1.py --config_file configs/stage2/resnext101a_384.yml MODEL.DEVICE_ID "('0')" OUTPUT_DIR './logs/stage2/resnext101a_384/v1'
python train_stage2_v2.py --config_file configs/stage2/resnext101a_384.yml MODEL.DEVICE_ID "('0')" OUTPUT_DIR './logs/stage2/resnext101a_384/v2'

python test.py --config_file configs/stage2/1resnext101a_384.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT './logs/stage2/resnext101a_384/v1/resnext101_ibn_a_2.pth' OUTPUT_DIR './logs/stage2/resnext101a_384/v1'
python test.py --config_file configs/stage2/resnext101a_384.yml MODEL.DEVICE_ID "('0')" TEST.WEIGHT './logs/stage2/resnext101a_384/v2/resnext101_ibn_a_2.pth' OUTPUT_DIR './logs/stage2/resnext101a_384/v2'

You should train camera and viewpoint models before the inference stage. You also can directly use our trained results (track_cam_rk.npy and track_view_rk.npy):

python train_cam.py --config_file configs/camera_view/camera_101a.yml
python train_view.py --config_file configs/camera_view/view_101a.yml

You can train all eight backbones by checking run.sh. Then, you can ensemble all results:

python ensemble.py

All trained models can be downloaded from here

Leaderboard

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{luo2021empirical,
 title={An Empirical Study of Vehicle Re-Identification on the AI City Challenge},
 author={Luo, Hao and Chen, Weihua and Xu Xianzhe and Gu Jianyang and Zhang, Yuqi and Chong Liu and Jiang Qiyi and He, Shuting and Wang, Fan and Li, Hao},
 booktitle={Proc. CVPR Workshops},
 year={2021}
}