The document outlines the MVTec AD dataset, a novel resource for unsupervised anomaly detection in industrial images, containing 3,629 training images and 1,725 testing images across 15 categories. Various methods for detecting anomalies are evaluated, including generative adversarial networks and autoencoders, with findings highlighting both strengths and weaknesses of these approaches. The study aims to set a benchmark for future research in industrial anomaly detection, indicating room for improvement in the methodology and evaluation metrics used.