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.

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

Anomalib inference with TensorRT (python).

Visual training and inference GUI for Anomalib

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

Defect Detection of Metal Nuts using anomalib library

Cpp-based onnx deployment, supporting yolo and unsupervised models

An industry-style proof of concept for welding anomaly detection using unsupervised PatchCore, featuring explainable heatmaps for visual interpretation of defects.

Toothbrush Anomalies Detector

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.