dfkde

Deep Feature Kernel Density Estimation

Model Type: Classification

Description

Fast anomaly classification algorithm that consists of a deep feature extraction stage followed by anomaly classification stage consisting of PCA and Gaussian Kernel Density Estimation.

Feature Extraction

Features are extracted by feeding the images through a ResNet50 backbone, which was pre-trained on ImageNet. The output of the penultimate layer (average pooling layer) of the network is used to obtain a semantic feature vector with a fixed length of 2048.

Anomaly Detection

In the anomaly classification stage, the features are first reduced to the first 16 principal components. Gaussian Kernel Density is then used to obtain an estimate of the probability density of new examples, based on the collection of training features obtained during the training phase.

Usage

python tools/train.py --model dfkde

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	0.762	0.646	0.577	0.669	0.965	0.863	0.951	0.751	0.698	0.806	0.729	0.607	0.694	0.767	0.839	0.866
Wide ResNet-50	0.774	0.708	0.422	0.905	0.959	0.903	0.936	0.746	0.853	0.736	0.687	0.749	0.574	0.697	0.843	0.892

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.872	0.864	0.844	0.854	0.960	0.898	0.942	0.793	0.908	0.827	0.894	0.916	0.859	0.853	0.756	0.916
Wide ResNet-50	0.875	0.907	0.844	0.905	0.945	0.914	0.946	0.790	0.914	0.817	0.894	0.922	0.855	0.845	0.722	0.910