Pipeline training and inference Anomalib models UI in Anomaly Detection

PatchCore method for Industrial Anomaly Detection + CLIP

Optimized PaDim based on Anodet

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.

📸🔥Thermal Camera Diagnosis App Prototype was my bachelor thesis project at THU, focused on enhancing battery recycling processes using thermal imaging anomaly detection.

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

Premium-quality notebooks for practical anomaly detection with Autoencoders, PatchCore, and DRÆM. Built for engineers, data teams, and SMEs adopting AI-powered inspection.

Industrial anomaly detection system using PatchCore-based feature extraction and memory bank modeling for visual defect localization.

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

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

Anomaly Detection with Localization developed for the Machine Learning for Vision and Multimedia course @polito. Tech stack: PyTorch, Python.

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.

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

Unsupervised visual anomaly detection using PatchCore on MVTec AD, achieves 99.1% AUROC on bottle defect detection without any anomaly labels during training

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

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.

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