MVTec-AC
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MVTec-AC is a curated refinement of the widely-used MVTec-AD dataset, specifically designed for anomaly classification—distinguishing between different types of anomalies rather than merely detecting if an image is anomalous. While MVTec-AD focuses on binary detection and suffers from mislabeled or ambiguous samples, MVTec-AC introduces manually corrected labels and reorganized anomaly categories to enable robust multi-class evaluation. Key improvements include the correction of 36 misclassified samples, merging of 4 overlapping classes, removal of 4 ambiguous ‘combined’ classes, and exclusion of the toothbrush category, which contains only a single trivial anomaly type. These changes support consistent, fine-grained assessment of classification models in industrial visual inspection contexts.
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