anomalib/models/cflow/README.md

Real-Time Unsupervised Anomaly Detection via Conditional Normalizing Flows

This is the implementation of the CFLOW-AD paper. This code is modified form of the official repository.

Model Type: Segmentation

Description

CFLOW model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. It consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders. The encoder extracts features with multi-scale pyramid pooling to capture both global and local semantic information with the growing from top to bottom receptive fields. Pooled features are processed by a set of decoders to explicitly estimate likelihood of the encoded features. The estimated multi-scale likelyhoods are upsampled to input size and added up to produce the anomaly map.

Architecture

Usage

python tools/train.py --model cflow

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	0.962	0.986	0.962	1.0	0.999	0.993	1.0	0.893	0.945	1.0	0.995	0.924	0.908	0.897	0.943	0.984

Pixel-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
Wide ResNet-50	0.971	0.986	0.968	0.993	0.968	0.924	0.981	0.955	0.988	0.990	0.982	0.983	0.979	0.985	0.897	0.980

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
Wide ResNet-50	0.944	0.972	0.932	1.000	0.988	0.967	1.000	0.832	0.939	1.000	0.979	0.924	0.971	0.870	0.818	0.967

Sample Results