-
Environment
torch == 1.9.1
torchvision == 0.10.1
numpy == 1.20.3
scipy == 1.7.1
sklearn == 1.0
PIL == 8.3.2
torch
torchvision == 0.10.1
opencv-python
matplotlib
scikit-image
pandas
numpy == 1.20.3
scipy == 1.7.1
scikit-learn == 1.0
Pillow == 8.3.2
- Dataset
You should download MVTec from MVTec AD: MVTec Software. The folder "mvtec" should be unpacked into the code folder.
- 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.
python3 main.py
@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}}