This repository provides a modular framework for unsupervised anomaly detection and localization on the MVTec Anomaly Detection (AD) dataset.
It implements several popular models for visual anomaly detection:
- Autoencoder (AE) – convolutional autoencoder with SSIM + MSE loss
- PaDiM – patch distribution modeling with multivariate Gaussians
- PatchCore – memory-bank nearest-neighbor matching on patch embeddings
- FastFlow – normalizing-flow–based density estimation on features
Utilities are included for data preparation, training, evaluation, and reporting, with metrics like Image-level AUROC, Pixel-level AUROC, AUPRC, and PRO.