{"url":"/dataset/aebad","name":"AeBAD","full_name":"Aero-engine Blade Anomaly Detection Dataset","description_markdown":"Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category.\r\n\r\nThe aim of AeBAD is to automatically detect abnormalities in the blades of aero-engines, ensuring their stable operation. AeBAD consists of two sub-datasets: the single-blade dataset (AeBAD-S) and the video anomaly detection of blades (AeBAD-V). AeBAD-S comprises images of single blades of different scales, with a primary feature being that the samples are not aligned. Furthermore, there is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. AeBAD-V, on the other hand, includes videos of blades assembled on the blisks of aero-engines, with the aim of detecting blade anomalies during blisk rotation. A distinctive feature of AeBAD-V is that the shooting view in the test set differs from that in the training set.","description_withheld":null,"homepage":"https://github.com/zhangzilongc/MMR","introduced_date":"2023-04-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/industrial-anomaly-detection-with-domain","title":"Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction","first_author":"Zilong Zhang","url":null},"license":null,"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":"Video Anomaly Detection","url":"/task/video-anomaly-detection","datasets_with_task":"/datasets/task/video-anomaly-detection"}],"languages":[],"variants":["AeBAD","AeBAD-S","AeBAD-V"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-aebad-s","task":"Anomaly Detection","dataset_variant":"AeBAD-S","rows":8,"metrics":["Detection AUROC","Segmentation AUPRO"],"first_row_in_archive_order":{"model":"MSFR","paper":"/paper/multi-scale-feature-reconstruction-network","metrics":{"Detection AUROC":"87.1","Segmentation AUPRO":"90.4"},"code_links":[{"title":"Ehteshamciitwah/MSFR","url":"https://github.com/Ehteshamciitwah/MSFR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-aebad-v","task":"Anomaly Detection","dataset_variant":"AeBAD-V","rows":7,"metrics":["Detection AUROC"],"first_row_in_archive_order":{"model":"MMR","paper":"/paper/industrial-anomaly-detection-with-domain","metrics":{"Detection AUROC":"78.2"},"code_links":[{"title":"zhangzilongc/MMR","url":"https://github.com/zhangzilongc/MMR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-anomaly-detection-on-aebad-s","task":"Unsupervised Anomaly Detection","dataset_variant":"AeBAD-S","rows":1,"metrics":["Detection AUROC"],"first_row_in_archive_order":{"model":"MSFR","paper":"/paper/multi-scale-feature-reconstruction-network","metrics":{"Detection AUROC":"87.1"},"code_links":[{"title":"Ehteshamciitwah/MSFR","url":"https://github.com/Ehteshamciitwah/MSFR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-scale-feature-reconstruction-network","title":"Multi-scale feature reconstruction network for industrial anomaly detection","date":"2024-10-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/industrial-anomaly-detection-with-domain","title":"Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction","date":"2023-04-05","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/anomaly-detection-via-reverse-distillation","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","date":"2022-01-26","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":14,"samples_unverified":4,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-supervised-out-of-distribution-detection-1","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","date":"2021-09-30","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","rows_on_this_dataset":2,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":36,"samples_ran":5,"samples_unverified":31,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inpainting-transformer-for-anomaly-detection","title":"Inpainting Transformer for Anomaly Detection","date":"2021-04-28","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/draem-a-discriminatively-trained-1","title":"DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection","date":"2021-01-01","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/reconstruction-by-inpainting-for-visual","title":"Reconstruction by Inpainting for Visual Anomaly Detection","date":"2020-10-17","rows_on_this_dataset":2,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":66,"samples_ran":25,"samples_unverified":41,"pointer_only_for_licence":19,"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."}