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
GitHub
https://github.com/xiahaifeng1995/STPM-Anomaly-Detection-Localization-master
