anomalib/models/stfpm/README.md

Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection

This is the implementation of the STFPM paper.

Model Type: Segmentation

Description

STFPM algorithm which consists of a pre-trained teacher network and a student network with identical architecture. The student network learns the distribution of anomaly-free images by matching the features with the counterpart features in the teacher network. Multi-scale feature matching is used to enhance robustness. This hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid thus allowing for anomaly detection of various sizes.

During inference, the feature pyramids of teacher and student networks are compared. Larger difference indicates a higher probability of anomaly occurrence.

Architecture

Usage

python tools/train.py --model stfpm

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
ResNet-18	0.893	0.954	0.982	0.989	0.949	0.961	0.979	0.838	0.759	0.999	0.956	0.705	0.835	0.997	0.853	0.645
Wide ResNet-50	0.876	0.957	0.977	0.981	0.976	0.939	0.987	0.878	0.732	0.995	0.973	0.652	0.825	0.5	0.875	0.899

Pixel-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.951	0.986	0.988	0.991	0.946	0.949	0.971	0.898	0.962	0.981	0.942	0.878	0.983	0.983	0.838	0.972
Wide ResNet-50	0.903	0.987	0.989	0.980	0.966	0.956	0.966	0.913	0.956	0.974	0.961	0.946	0.988	0.178	0.807	0.980

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.932	0.961	0.982	0.989	0.930	0.951	0.984	0.819	0.918	0.993	0.973	0.918	0.887	0.984	0.790	0.908
Wide ResNet-50	0.926	0.973	0.973	0.974	0.965	0.929	0.976	0.853	0.920	0.972	0.974	0.922	0.884	0.833	0.815	0.931

Sample Results