PatchCore anomaly detection

Unofficial implementation of PatchCore(new SOTA) anomaly detection model

Original Paper : Towards Total Recall in Industrial Anomaly Detection (Jun 2021)
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler

https://arxiv.org/abs/2106.08265

notice(21/06/18) :
This code is not yet verified. Any feedback is appreciated.

Usage

# python 3.6
pip install -r requirements.txt
python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 3'

MVTecAD AUROC score (PatchCore-1%, mean of n trials)

Category	Paper (image-level)	This code (image-level)	Paper (pixel-level)	This code (pixel-level)
carpet	0.980	0.995(1)	0.989	0.989(1)
grid	0.986	0.899(1)	0.986	0.978(1)
leather	1.000	1.000	0.993	0.992(1)
tile	0.994	0.981(1)	0.961	0.932(1)
wood	0.992	-	0.951	-
bottle	1.000	-	0.985	-
cable	0.993	-	0.982	-
capsule	0.980	-	0.988	-
hazelnut	1.000	-	0.986	-
metal nut	0.997	-	0.984	-
pill	0.970	-	0.971	-
screw	0.964	-	0.992	-
toothbrush	1.000	-	0.985	-
transistor	0.999	-	0.949	-
zipper	0.992	-	0.988	-
mean	0.990	-	0.980	-

Code Reference

https://github.com/google/active-learning
https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master