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

TeamName	mAP	Link
DMT(Ours)	0.7445	code
NewGeneration	0.7151	code
CyberHu	0.6550	code

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}
}