{"url":"/dataset/mvtecad","name":"MVTecAD","full_name":"MVTEC ANOMALY DETECTION DATASET","description_markdown":"MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.\r\n\r\nThere are two common metrics: Detection AUROC and Segmentation (or pixelwise) AUROC\r\n\r\nDetection (or, classification) methods output single float (anomaly score) per input test image. \r\n\r\nSegmentation methods output anomaly probability for each pixel. \r\n\"To assess segmentation performance, we evaluate the relative per-region overlap of the segmentation with the ground truth. To get an additional performance measure that is independent of the determined threshold, we compute the area under the receiver operating characteristic curve (ROC AUC). We define the true positive rate as the percentage of pixels that were correctly classified as anomalous\" [1]\r\nLater segmentation metric was improved to balance regions with small and large area, see PRO-AUC and other in [2]\r\nSource: [MVTEC ANOMALY DETECTION DATASET](https://www.mvtec.com/company/research/datasets/mvtec-ad/)\r\nImage Source: [https://www.mvtec.com/company/research/datasets/mvtec-ad/](https://www.mvtec.com/company/research/datasets/mvtec-ad/)\r\n[1] Paul Bergmann et al, \"MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection\"\r\n[2] [Bergmann, P., Batzner, K., Fauser, M. et al. The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. Int J Comput Vis (2021). https://doi.org/10.1007/s11263-020-01400-4](https://link.springer.com/article/10.1007/s11263-020-01400-4)","description_withheld":null,"homepage":"https://www.mvtec.com/company/research/datasets/mvtec-ad/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/mvtec-ad-a-comprehensive-real-world-dataset","title":"MVTec AD -- A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection","first_author":"Paul Bergmann","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://www.mvtec.com/company/research/datasets/mvtec-ad/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Outlier Detection","url":"/task/outlier-detection","datasets_with_task":"/datasets/task/outlier-detection"},{"name":"Anomaly Classification","url":"/task/anomaly-classification","datasets_with_task":"/datasets/task/anomaly-classification"},{"name":"Supervised Anomaly Detection","url":"/task/supervised-anomaly-detection","datasets_with_task":"/datasets/task/supervised-anomaly-detection"},{"name":"zero-shot anomaly detection","url":"/task/zero-shot-anomaly-detection","datasets_with_task":"/datasets/task/zero-shot-anomaly-detection"},{"name":"Multi-class Anomaly Detection","url":"/task/multi-class-anomaly-detection","datasets_with_task":"/datasets/task/multi-class-anomaly-detection"}],"languages":[],"variants":["MVTec AD","MVTecAD"],"data_loaders":[],"num_papers_in_archive":402,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset_variant":"MVTec AD","rows":148,"metrics":["Detection AUROC","Segmentation AUPRO","Segmentation AUROC","Segmentation AP","FPS"],"first_row_in_archive_order":{"model":"GLASS","paper":"/paper/a-unified-anomaly-synthesis-strategy-with","metrics":{"Detection AUROC":"99.9","Segmentation AUPRO":"96.8","Segmentation AUROC":"99.3"},"code_links":[{"title":"cqylunlun/glass","url":"https://github.com/cqylunlun/glass"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-class-anomaly-detection-on-mvtec-ad","task":"Multi-class Anomaly Detection","dataset_variant":"MVTec AD","rows":13,"metrics":["Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"INP-Former-Large","paper":"/paper/exploring-intrinsic-normal-prototypes-within","metrics":{"Detection AUROC":"99.8","Segmentation AUROC":"98.6"},"code_links":[{"title":"luow23/inp-former","url":"https://github.com/luow23/inp-former"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/supervised-anomaly-detection-on-mvtec-ad","task":"Supervised Anomaly Detection","dataset_variant":"MVTec AD","rows":8,"metrics":["Detection AUROC","Segmentation AUROC","Segmentation AUPRO","Segmentation AP"],"first_row_in_archive_order":{"model":"WeakREST-Block","paper":"/paper/efficient-anomaly-detection-with-budget","metrics":{"Detection AUROC":"99.8","Segmentation AP":"87.6","Segmentation AUPRO":"98.4","Segmentation AUROC":"99.7"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-classification-on-mvtecad","task":"Anomaly Classification","dataset_variant":"MVTecAD","rows":2,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"VELM","paper":"/paper/detect-classify-act-categorizing-industrial","metrics":{"Accuracy (% )":"81.4"},"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/center-aware-residual-anomaly-synthesis-for","title":"Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection","date":"2025-05-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"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":1,"code_links":1,"syntology":null},{"paper":"/paper/reconstruction-free-anomaly-detection-with-1","title":"Reconstruction-Free Anomaly Detection with Diffusion Models via Direct Latent Likelihood Evaluation","date":"2025-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exploring-intrinsic-normal-prototypes-within","title":"Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection","date":"2025-03-04","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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