README.md

PatchCore

This is the implementation of the PatchCore paper.

Model Type: Segmentation

Description

The PatchCore algorithm is based on the idea that an image can be classified as anomalous as soon as a single patch is anomalous. The input image is tiled. These tiles act as patches which are fed into the neural network. It consists of a single pre-trained network which is used to extract "mid" level features patches. The "mid" level here refers to the feature extraction layer of the neural network model. Lower level features are generally too broad and higher level features are specific to the dataset the model is trained on. The features extracted during training phase are stored in a memory bank of neighbourhood aware patch level features.

During inference this memory bank is coreset subsampled. Coreset subsampling generates a subset which best approximates the structure of the available set and allows for approximate solution finding. This subset helps reduce the search cost associated with nearest neighbour search. The anomaly score is taken as the maximum distance between the test patch in the test patch collection to each respective nearest neighbour.

Architecture

Usage

anomalib train --model Patchcore --data MVTec --data.category <category>

Benchmark

All results gathered with seed 42.

MVTec AD Dataset

Image-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
Wide ResNet-50	0.980	0.984	0.959	1.000	1.000	0.989	1.000	0.990	0.982	1.000	0.994	0.924	0.960	0.933	1.000	0.982
ResNet-18	0.973	0.970	0.947	1.000	0.997	0.997	1.000	0.986	0.965	1.000	0.991	0.916	0.943	0.931	0.996	0.953

Pixel-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
Wide ResNet-50	0.980	0.988	0.968	0.991	0.961	0.934	0.984	0.988	0.988	0.987	0.989	0.980	0.989	0.988	0.981	0.983
ResNet-18	0.976	0.986	0.955	0.990	0.943	0.933	0.981	0.984	0.986	0.986	0.986	0.974	0.991	0.988	0.974	0.983

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
Wide ResNet-50	0.976	0.971	0.974	1.000	1.000	0.967	1.000	0.968	0.982	1.000	0.984	0.940	0.943	0.938	1.000	0.979
ResNet-18	0.970	0.949	0.946	1.000	0.982	0.992	1.000	0.978	0.969	1.000	0.989	0.940	0.932	0.935	0.974	0.967

Sample Results