Pipeline training and inference Anomalib models UI in Anomaly Detection

PatchCore method for Industrial Anomaly Detection + CLIP

An unofficial implementation using Pytorch for "Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types". Improve the algorithm with DINO pretrained ViT. Implement algorithms based on PatchCore.

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

Unsupervised anomaly detection pipeline for real-time welding quality control with IATF 16949 audit trail compliance.

Edge anomaly detection for video streams with multi-backend inference benchmarking (TensorRT, ONNX, PyTorch)

Modular 2D industrial inspection platform with a swappable method interface — two anomaly-detection methods (autoencoder + PatchCore) built from scratch, served via FastAPI + React, Dockerized. Benchmarked on MVTec AD (mean image AUROC 0.72 → 0.96).

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

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

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

A PyTorch-based framework for unsupervised anomaly detection on the MVTec AD dataset. Implements PaDiM, PatchCore, FastFlow, and AutoEncoder models with utilities for dataset preparation, training, evaluation, and reporting.

Visual quality inspection using PatchCore deep anomaly detection and classical SSIM.

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

Training-free deep learning for unsupervised industrial defect detection & localization on MVTec AD, PatchCore & PaDiM, with image and pixel level AUROC and a one-click free-Colab demo.

Industrial anomaly detection: PatchCore, FastFlow, EfficientAD on MVTec benchmark

Clean reproduction of PatchCore (Roth et al., CVPR 2022) on MVTec-AD with image-level AUROC, pixel-level AUROC, and pixel-level AUPRO. First in a series of visual anomaly detection reproductions.

正常品だけで学ぶ外観検査の異常検知。PatchCore方式でCPU動作、AUROC評価とヒートマップ可視化付き

Unsupervised industrial defect detection using PatchCore on MVTec AD. Achieves AU-ROC 0.904 and AU-PRO 0.917. Includes FastAPI inference service and Gradio demo.

OrbitCore: Perspective- and illumination-invariant PatchCore for dynamic environments.

Build and run 2D industrial visual-inspection pipelines in a visual node editor — anomaly detection, YOLO, template matching, caliper, blob/color, OCR. Offline Python/PySide6 desktop app with a headless MCP server and ForgeAssist, an in-app Claude/ChatGPT/Gemini assistant.