BGAD: A Python repository from guome

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

PyTorch implementation and for CVPR2023 paper, Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection.

Installation

Install all packages with this command:

$ python3 -m pip install -U -r requirements.txt

Download Datasets

Please download MVTecAD dataset from MVTecAD dataset and BTAD dataset from BTAD dataset.

Training

Run code for training MVTecAD

python main.py --flow_arch conditional_flow_model --gpu 0 --data_path /path/to/your/dataset --with_fas --data_strategy 0,1 --num_anomalies 10 --not_in_test --exp_name bgad_fas_10 --focal_weighting --pos_beta 0.01 --margin_tau 0.1

Run code for training BTAD

python main.py --flow_arch conditional_flow_model --gpu 0 --dataset btad --data_path /path/to/your/dataset --with_fas --data_strategy 0,1 --num_anomalies 10 --not_in_test --exp_name bgad_fas_10 --focal_weighting --pos_beta 0.01 --margin_tau 0.1

Testing

Run code for testing

python test.py --flow_arch conditional_flow_model --gpu 0 --checkpoint /path/to/output/dir --phase test --pro

Citation

If you find this repository useful, please consider citing our work:

@article{BGAD,
      title={Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection}, 
      author={Xincheng Yao and Ruoqi Li and Jing Zhang and Jun Sun and Chongyang Zhang},
      year={2023},
      booktitle={Conference on Computer Vision and Pattern Recognition 2023},
      url={https://arxiv.org/abs/2207.01463},
      primaryClass={cs.CV}
}

Acknowledgement

This repository is built using the timm library, the CFLOW repository and the FrEIA repository.

guome/BGAD