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.

[NeurIPS 2022 Spotlight] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

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).

We have summarised all 3D anomaly detection methods and datasets (still updating). 多模态，点云和姿势无关异常检测的综述仓库（持续更新）

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

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

[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

[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

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"

This is an official implementation of “ Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection” (PCSNet) with PyTorch.

Production-ready visual anomaly detection from normal images, featuring PaDiM, ultra-light PatchCore, automated edge deployment, and optimized ONNX, OpenVINO, TensorRT INT8, Hailo, and KV260 support.

This is a cross-modal benchmark for industrial 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.