{"url":"/dataset/btad","name":"BTAD","full_name":"beanTech Anomaly Detection","description_markdown":"The **BTAD** ( beanTech Anomaly Detection) dataset is a real-world industrial anomaly dataset. The dataset contains a total of 2830 real-world images of 3 industrial products showcasing body and surface defects.","description_withheld":null,"homepage":"http://avires.dimi.uniud.it/papers/btad/btad.zip","introduced_date":"2021-04-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/vt-adl-a-vision-transformer-network-for-image","title":"VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization","first_author":"Pankaj Mishra","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Supervised Anomaly Detection","url":"/task/supervised-anomaly-detection","datasets_with_task":"/datasets/task/supervised-anomaly-detection"}],"languages":[],"variants":["BTAD"],"data_loaders":[{"repo":"https://github.com/openvinotoolkit/anomalib","url":"https://github.com/openvinotoolkit/anomalib","frameworks":["pytorch"]},{"repo":"https://github.com/pankajmishra000/VT-ADL","url":"https://github.com/pankajmishra000/VT-ADL","frameworks":["pytorch"]}],"num_papers_in_archive":61,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-btad","task":"Anomaly Detection","dataset_variant":"BTAD","rows":15,"metrics":["Detection AUROC","Segmentation AUROC","Segmentation AP","Segmentation AUPRO"],"first_row_in_archive_order":{"model":"UniNet","paper":"/paper/uninet-a-contrastive-learning-guided-unified","metrics":{"Detection AUROC":"97.73","Segmentation AUPRO":"80.01","Segmentation AUROC":"97.70"},"code_links":[{"title":"pangdatangtt/UniNet","url":"https://github.com/pangdatangtt/UniNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/supervised-anomaly-detection-on-btad","task":"Supervised Anomaly Detection","dataset_variant":"BTAD","rows":2,"metrics":["Detection AUROC","Segmentation AP","Segmentation AUPRO","Segmentation AUROC"],"first_row_in_archive_order":{"model":"CPR","paper":"/paper/target-before-shooting-accurate-anomaly","metrics":{"Detection AUROC":"98.3","Segmentation AP":"84.0","Segmentation AUPRO":"91.4","Segmentation AUROC":"99.1"},"code_links":[{"title":"flyinghu123/cpr","url":"https://github.com/flyinghu123/cpr"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/uninet-a-contrastive-learning-guided-unified","title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","date":"2025-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unlocking-the-potential-of-reverse","title":"Unlocking the Potential of Reverse Distillation for Anomaly Detection","date":"2024-12-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/anomaly-detection-using-normalizing-flow","title":"Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification","date":"2024-05-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/realnet-a-feature-selection-network-with","title":"RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection","date":"2024-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":9,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/musc-zero-shot-industrial-anomaly","title":"MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images","date":"2024-01-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/d3ad-dynamic-denoising-diffusion","title":"Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection","date":"2024-01-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/target-before-shooting-accurate-anomaly","title":"Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval","date":"2023-08-13","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-anomaly-detection-with-budget","title":"Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers","date":"2023-06-06","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/reconpatch-contrastive-patch-representation","title":"ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection","date":"2023-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pyramidflow-high-resolution-defect","title":"PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow","date":"2023-03-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/revisiting-reverse-distillation-for-anomaly","title":"Revisiting Reverse Distillation for Anomaly Detection","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/image-anomaly-detection-and-localization-with","title":"PNI : Industrial Anomaly Detection using Position and Neighborhood Information","date":"2022-11-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/altub-alternating-training-method-to-update","title":"AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection","date":"2022-10-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/vt-adl-a-vision-transformer-network-for-image","title":"VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization","date":"2021-04-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/patch-svdd-patch-level-svdd-for-anomaly","title":"Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation","date":"2020-06-29","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":35,"samples_ran":16,"samples_unverified":19,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}