CLendering/SubspaceAD

Official implementation for AnomalyCLIP (ICLR 2024)

Official implementation of CVPR'24 paper 'Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts'.

Vanilla torch and timm industrial knn-based anomaly detection for images.

[CVPR 2025] Official Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection". The first multi-class UAD model that can compete with single-class SOTAs

Pytorch version of - https://github.com/WaqasSultani/AnomalyDetectionCVPR2018

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

Use DINOv3’s powerful, self-supervised visual features + YOLOv12’s blazing-fast detection, all in one repo. Whether you have only a few hundred labeled images or a medium-sized dataset, DINOV3-YOLOV12 helps you get better detection accuracy — without sacrificing speed. Great for niche domains, small-data projects, or quick prototyping.

👽 Out-of-Distribution Detection with PyTorch

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)。