{"url":"/dataset/mvtec-ac","name":"MVTec-AC","full_name":null,"description_markdown":"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.","description_withheld":null,"homepage":"https://github.com/Sassanmtr/VELM","introduced_date":"2025-05-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/detect-classify-act-categorizing-industrial","title":"Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models","first_author":"Sassan Mokhtar","url":null},"license":{"name":"MIT License","url":"https://github.com/Sassanmtr/VELM/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Classification","url":"/task/anomaly-classification","datasets_with_task":"/datasets/task/anomaly-classification"},{"name":"Anomaly Severity Classification (Anomaly vs. Defect)","url":"/task/anomaly-severity-classification-anomaly-vs","datasets_with_task":"/datasets/task/anomaly-severity-classification-anomaly-vs"}],"languages":[],"variants":["MVTec-AC"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-classification-on-mvtec-ac","task":"Anomaly Classification","dataset_variant":"MVTec-AC","rows":1,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"VELM","paper":"/paper/detect-classify-act-categorizing-industrial","metrics":{"Accuracy (% )":"84.0"},"code_links":[{"title":"sassanmtr/velm","url":"https://github.com/sassanmtr/velm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-severity-classification-anomaly-vs","task":"Anomaly Severity Classification (Anomaly vs. Defect)","dataset_variant":"MVTec-AC","rows":1,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"VELM","paper":"/paper/detect-classify-act-categorizing-industrial","metrics":{"Accuracy (% )":"89.8"},"code_links":[{"title":"sassanmtr/velm","url":"https://github.com/sassanmtr/velm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/detect-classify-act-categorizing-industrial","title":"Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models","date":"2025-05-05","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}