{"url":"/dataset/domain-independent-anomalies-datasets","name":"Domain-independent anomalies datasets","full_name":"Domain-independent anomalies datasets (adaptions of the MVTec Anomaly Detection dataset)","description_markdown":"An adaption of the MVTec Anomaly Detection dataset, presented in the paper \"Domain-independent detection of known anomalies\".\r\n\r\nThere are three different datasets, each covering one specific anomaly type: _color_, _cut_ and _hole_.\r\nThe datasets can be used to evaluate approaches on the hybrid task of detecting known anomalies across different, previously unseen objects:\r\nAll object types except one are used for training. During testing, the images of the remaining object type should be classified on whether they contain an anomaly.\r\n\r\n_Note: the authors are not affiliated with MVTec_","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.11920346","introduced_date":"2024-07-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/domain-independent-detection-of-known","title":"Domain-independent detection of known anomalies","first_author":"Jonas Bühler","url":null},"license":{"name":"Creative Commons  Attribution-NonCommercial-ShareAlike 4.0 International License","url":"http://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"task","url":null,"datasets_with_task":"/datasets/task/task"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"}],"languages":[],"variants":["Domain-independent anomalies datasets"],"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/on-domain-independent-anomalies-datasets","task":"","dataset_variant":"Domain-independent anomalies datasets","rows":2,"metrics":["F1-score","Detection AUROC"],"first_row_in_archive_order":{"model":"Spatial Embedding MLP (ViT-B/8)","paper":"/paper/domain-independent-detection-of-known","metrics":{"Detection AUROC":"86.7","F1-score":"85"},"code_links":[{"title":"Jonas1302/anomalib","url":"https://github.com/Jonas1302/anomalib"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/domain-independent-detection-of-known","title":"Domain-independent detection of known anomalies","date":"2024-07-03","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}