anomalib

Pipeline training and inference Anomalib models UI in Anomaly Detection

[ 开箱即用 ] 面向工业视觉检测的一站式平台，基于 ASP.NET Core 集成 YOLO、Anomalib 与 SAM，提供智能辅助标注、模型训练、异常定位、缺陷识别、联合验证和 API 服务。

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.

Anomalib inference with TensorRT (python).

Visual training and inference GUI for Anomalib

Anomaly Detection Using Anomalib and OpenVINO – Step by Step by Guide

Comprehensive benchmark for anomaly detection models on MVTec AD dataset using Anomalib. Includes PatchCore, EfficientAD, FastFlow, STFPM, PaDiM with Gradio demo.

Real-time manufacturing defect detection using unsupervised PatchCore — FastAPI + Streamlit + OpenVINO

Official ISP-AD Dataset repository, providing PyTorch & Anomalib evaluation pipelines for industrial visual anomaly detection.

兼容 Anomalib 生态的通用异常检测推理库：一张图进去，缺陷 mask + 分数 + OK/NG 出来，一个 DLL + 一个头文件，零 OpenCV 依赖。

Industrial visual anomaly detection using PatchCore with FastAPI, Streamlit and Docker deployment.

Based on the Screw, Bottle or anyother image uploaded, the model will analyse the damage occured in the part.

Unsupervised anomaly detection on MVTec AD metal categories using PatchCore (Anomalib). AUROC 0.9976 on metal_nut.

Cpp-based onnx deployment, supporting yolo and unsupervised models

Uses computer vision to detect defects in manufactured products, with experiments showing where the models work and where they struggle.

Anomaly detection in idustrial images: final degree thesis, anomalib library benchmark, and reproducible inference pipeline.

A focused fork of an anomaly detection library, dedicated to enhancing and extending the PatchCore model.

Defect Detection of Metal Nuts using anomalib library