Awesome

FAVAE anomaly detection

This is an implementation of the paper Anomaly localization by modeling perceptual features

<p align="center"> <img src="imgs/pic1.jpg" width="600"\> </p>

Requirement

python == 3.7
pytorch == 1.5
tqdm
sklearn
matplotlib

Datasets

MVTec AD datasets : Download from MVTec website

Code example

bottle

python train.py --obj bottle --do_aug

Results

<p align="center"> <img src="imgs/pic2.jpg" width="600"\> </p> <p align="center"> <img src="imgs/pic3.jpg" width="600"\> </p> <p align="center"> <img src="imgs/pic4.jpg" width="600"\> </p>

Reference

[1] David Dehaene, Pierre Eline. Anomaly localization by modeling perceptual features. https://arxiv.org/pdf/2008.05369.pdf

[2] https://github.com/byungjae89/SPADE-pytorch

[3] https://github.com/plutoyuxie/AutoEncoder-SSIM-for-unsupervised-anomaly-detection-