Pipeline training and inference Anomalib models UI in Anomaly Detection

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.

detect and segment anomalies on wood surfaces using unsupervised deep learning models

Governed, reproducible visual-inspection runtime using PaDiM, ONNX Runtime, deterministic replay, and hash-anchored evidence.

PaDiM-inspired anomaly detection for AWS infrastructure metrics (CloudWatch + Cost Explorer)

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.