Pipeline training and inference Anomalib models UI in Anomaly Detection

Anomaly detection pipeline for industrial surface inspection using the MVTec AD dataset. Includes classical computer vision baselines, autoencoder models, and PaDiM-based feature embeddings for detecting subtle manufacturing defects.

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

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.