anomalib

Pipeline training and inference Anomalib models UI in Anomaly Detection

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.

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

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。

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

Toothbrush Anomalies Detector

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

Industrial visual anomaly detection with PatchCore on five MVTec AD categories, controlled experiments, failure analysis, testing, and reusable validation.

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

Defect Detection of Metal Nuts using anomalib library

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

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

Anomalies Detector - Bent wire

This project utilizes anoamlib, a powerful anomaly detection library, to detect anomalies in capacitors based on their electrical characteristics.