An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

This is an unofficial implementation of the paper “PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization”.

Unofficial implementation of EfficientAD https://arxiv.org/abs/2303.14535

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

GeneralAD

Shape-Guided Dual-Memory Learning for 3D Anomaly Detection [ICML2023]

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.

This is an unofficial implementation of ' Anomaly localization by modeling perceptual features'

Anomaly localization using autoencoder models in the feature space of a ResNet

EfficientNetV2 based PaDiM

[TF 2.x] PaDiM - unofficial tensorflow implementation of the paper 'a Patch Distribution Modeling Framework for Anomaly Detection and Localization'.

[IJCV 2024] Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection