shubhamkriitr/DRA

Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection.

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection (CVPR2022)

By Choubo Ding, Guansong Pang, Chunhua Shen

Official PyTorch implementation of "Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection".

Prerequisites

This code is written in Python 3.7 and requires the packages listed in requirements.txt. Install with pip install -r requirements.txt preferably in a virtualenv.

Run

Step 1. Setup the Anomaly Detection Dataset

Download the Anomaly Detection Dataset and convert it to MVTec AD format. (For datasets we used in the paper, we provided the convert script.) The dataset folder structure should look like:

DATA_PATH/
    subset_1/
        train/
            good/
        test/
            good/
            defect_class_1/
            defect_class_2/
            defect_class_3/
            ...
    ...

Step 2. Running DRA

python train.py --dataset_root=./data/mvtec_anomaly_detection \
                --classname=carpet \
                --experiment_dir=./experiment

• dataset_root denotes the path of the dataset.
• classname denotes the subset name of the dataset.
• experiment_dir denotes the path to store the experiment setting and model weight.
• outlier_root (*optional) given the path of the outlier dataset to disable pseudo augmentation and enable external data for pseudo head.
• know_class (*optional) specify the anomaly class in the training set to experiment within the hard setting.

Citation

@inproceedings{ding2022catching,
      title={Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection}, 
      author={Ding, Choubo and Pang, Guansong and Shen, Chunhua},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      year={2022}
}