This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

Related tags

Deep Learning STPM-Anomaly-Detection-Localization-master
Overview

STPM-Anomaly-Detection-Localization-master

This is an implementation of the paper Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection.

Datasets

MVTec AD datasets : Download from MVTec website

Environment

pip install -r requirements.txt

Usage

python main.py --phase 'train or test' --dataset_path 'D:/dataset/mvtec_anomaly_detection' --save_path 'path\to\save\results' --obj 'class name'

MVTecAD AUC-ROC score (mean of n trials)

Category Paper
(pixel-level)		 This code
(pixel-level)		 Paper
(image-level)		 This code
(image-level)
carpet 0.988 0.988(1) - 0.999(1)
grid 0.990 0.980(1) - 0.925(1)
leather 0.993 0.989(1) - 1.0(1)
tile 0.974 0.919(1) - 0.979(1)
wood 0.972 0.926(1) - 0.988(1)
bottle 0.988 0.973(1) - 0.993(1)
cable 0.955 0.971(1) - 0.995(1)
capsule 0.983 0.963(1) - 0.818(1)
hazelnut 0.985 0.971(1) - 0.975(1)
metal nut 0.976 0.963(1) - 0.995(1)
pill 0.978 0.934(1) - 0.887(1)
screw 0.983 0.961(1) - 0.806(1)
toothbrush 0.989 0.978(1) - 0.989(1)
transistor 0.825 0.921(1) - 0.978(1)
zipper 0.985 0.969(1) - 0.899(1)
mean 0.970 0.960(1) 0.955 0.948(1)

Visualization examples

Acknowledgement

The code is partially adapted from STPM_anomaly_detection

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Comments
  • 关于loss的问题

    关于loss的问题

    1. 目前的loss计算方法如下 f_loss = 0.5 * (ft_norm - fs_norm)**2 ave_loss = f_loss.sum() / (h*w) t_loss += ave_loss

    2. 我感觉这样做有些不合理，只是统计意义的最小，但是针对一些局部细节，并不一定能够做到很好。是否要考虑max value的限制呢

    opened by TimZhang001 0
  • Pretrained weights ?

    Pretrained weights ?

    Dear @xiahaifeng1995 , Thank you for such a nice implementation, your result is extremely great. I want to test it but I dont see where the pretrained weight is ?

    opened by trungpham2606 2
Owner
haifeng xia
haifeng xia
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