  Visual anomaly detection is essential and commonly used for many tasks in the
field of computer vision. Recent anomaly detection datasets mainly focus on
industrial automated inspection, medical image analysis and video surveillance.
In order to broaden the application and research of anomaly detection in
unmanned supermarkets and smart manufacturing, we introduce the supermarket
goods anomaly detection (GoodsAD) dataset. It contains 6124 high-resolution
images of 484 different appearance goods divided into 6 categories. Each
category contains several common different types of anomalies such as
deformation, surface damage and opened. Anomalies contain both texture changes
and structural changes. It follows the unsupervised setting and only normal
(defect-free) images are used for training. Pixel-precise ground truth regions
are provided for all anomalies. Moreover, we also conduct a thorough evaluation
of current state-of-the-art unsupervised anomaly detection methods. This
initial benchmark indicates that some methods which perform well on the
industrial anomaly detection dataset (e.g., MVTec AD), show poor performance on
our dataset. This is a comprehensive, multi-object dataset for supermarket
goods anomaly detection that focuses on real-world applications.
