adhirajghosh / rptm_reid Goto Github PK

1. Install CUDA compatible torch. Modify based on CUDA version.

conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia

2. Install other dependencies.

pip install -r requirements.txt

3. Install apex (optional but recommended)

Follow the installation guidelines from https://github.com/NVIDIA/apex Then set SOLVER.USE_AMP as True in the config files directly or via command line.

mkdir data

Download the vehicle reID datasets VehicleID and VeRi-776, and the person reID datasets DukeMTMC-reID. Follow the structure and naming convention as below.

data
├── duke
│   └── images ..
├── vehicleid
│   └── images ..
└── veri
    └── images ..

mkdir gms

You need to download the GMS feature matches for VeRi, VehicleID and DukeMTMC: GMS.

The folder should follow the structure as shown below:

gms
├── duke
│   └── 0001.pkl ..
├── vehicleid
│   └── 00001.pkl ..
└── veri
    └── 001.pkl ..

1. Training

python main.py --config_file configs/veri_r101.yml 

The above command trains a baseline using our RPTM algorithm for VeRi. Note that after training, the model provides evaluation results, both qualitative as well as quantitative.

2. RPTM Thresholding Strategies

In Section 4.2 of our paper, we defined a thresholding strategy for better anchor-positive selections. We define this in config files as MODEL.RPTM_SELECT. While it is set to 'mean', feel free to work with 'min' and 'max'.

python main.py --config_file configs/veri_r101.yml MODEL.RPTM_SELECT 'min'

python main.py --config_file configs/veri_r101.yml MODEL.RPTM_SELECT 'max'

3. Testing

mkdir logs
python main.py --config_file configs/veri_r101.yml TEST.WEIGHT '<path to trained model>' TEST.EVAL True 

1. VeRi776: 88.0%
2. VehicleID (query size 800): 84.8%
3. VehicleID (query size 1600): 81.2%
4. VehicleID (query size 2400): 80.5%
5. DukeMTMC: 89.2%

GMS Feature Matching Algorithm taken from: https://github.com/JiawangBian/GMS-Feature-Matcher

If you find this code useful for your research, please cite our paper

@InProceedings{Ghosh_2023_WACV,
    author    = {Ghosh, Adhiraj and Shanmugalingam, Kuruparan and Lin, Wen-Yan},
    title     = {Relation Preserving Triplet Mining for Stabilising the Triplet Loss In re-Identification Systems},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {4840-4849}
}

If you have any questions, please feel free to contact us. E-mail: Adhiraj Ghosh , Wen-Yan Lin