Popular GitHub Repositories for Anomaly Segmentation

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

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

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.

[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper (VAND Workshop - CVPR 2023).

[AAAI-2024] Offical code for <Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt>.

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

Project: Unsupervised Anomaly Segmentation via Deep Feature Reconstruction

Official code for 'Deep One-Class Classification via Interpolated Gaussian Descriptor' [AAAI 2022 Oral]

Implementation of our paper "Optimizing PatchCore for Few/many-shot Anomaly Detection"

Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

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

Unsupervised Anomaly Detection and Segmentation via Deep Feature Correspondence

[GCPR 2023] UGainS: Uncertainty Guided Anomaly Instance Segmentation

Learning Diffusion Models for Multi-View Anomaly Detection [ECCV2024]

Official implementation of the paper "Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI" accepted to the MICCAI 2021 BrainLes workshop

This is a cross-modal benchmark for industrial anomaly detection.

This is a code implemention for paper "Self-Attention Autoencoder for Anomaly Segmentation"