hq-deng / RD4AD

Anomaly Detection via Reverse Distillation from One-Class Embedding

CVPR2022 - Anomaly Detection via Reverse Distillation from One-Class Embedding

Implementation (Official Code ⭐️ ⭐️ ⭐️ )

1. Environment

   pytorch == 1.91

   torchvision == 0.10.1

   numpy == 1.20.3

   scipy == 1.7.1

   sklearn == 1.0

   PIL == 8.3.2

2. Dataset

   You should download MVTec from MVTec AD: MVTec Software. The folder "mvtec" should be unpacked into the code folder.

3. Train and Test the Model We have write both training and evaluation function in the main.py, execute the following command to see the training and evaluation results.

   python main.py

Reference

@InProceedings{Deng_2022_CVPR,
author    = {Deng, Hanqiu and Li, Xingyu},
title     = {Anomaly Detection via Reverse Distillation From One-Class Embedding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month     = {June},
year      = {2022},
pages     = {9737-9746}}