VisA-AC

VisA-AC is a refined benchmark based on the VisA dataset, tailored for the task of anomaly classification—distinguishing between different types of anomalies rather than simply detecting whether an image is anomalous. While the original VisA provides anomaly type information in an Excel file, it includes numerous under-sampled and ambiguous classes. VisA-AC addresses these issues by removing classes with fewer than 10 samples, merging visually similar categories, and manually correcting mislabeled samples. Additionally, anomaly classes in VisA-AC are organized into separate folders—following the structure of MVTec-AC—for easier integration and usage. The resulting dataset ensures both statistical robustness and semantic clarity, supporting rigorous evaluation of multi-class anomaly classification methods in real-world industrial settings.