Omkar2118/datasets huggingface dataset:
MVTec-AC

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.

Introducing paper: Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models
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.

Introducing paper: Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models