{"url":"/dataset/dagm2007","name":"DAGM2007","full_name":"DAGM2007","description_markdown":"This is a synthetic dataset for defect detection on textured surfaces. It was originally created for a competition at the 2007 symposium of the DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., the German chapter of the International Association for Pattern Recognition). The competition was hosted together with the GNSS (German Chapter of the European Neural Network Society).\r\n\r\nAfter the competition, the dataset has been used as a test dataset in multiple projects and research papers. It is publicly available from the University of Heidelberg website (Heidelberg Collaboratory for Image Processing).\r\n\r\nThe data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.","description_withheld":null,"homepage":"https://conferences.mpi-inf.mpg.de/dagm/2007/prizes.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Defect Detection","url":"/task/defect-detection","datasets_with_task":"/datasets/task/defect-detection"},{"name":"Weakly Supervised Defect Detection","url":"/task/weakly-supervised-defect-detection","datasets_with_task":"/datasets/task/weakly-supervised-defect-detection"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["DAGM2007"],"data_loaders":[{"repo":"https://github.com/azzaelnaggar/data","url":"https://github.com/azzaelnaggar/data","frameworks":[]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-dagm2007","task":"Unsupervised Anomaly Detection","dataset_variant":"DAGM2007","rows":1,"metrics":["Detection AUROC"],"first_row_in_archive_order":{"model":"DiffusionAD","paper":"/paper/diffusionad-denoising-diffusion-for-anomaly","metrics":{"Detection AUROC":"99.6"},"code_links":[{"title":"huizhang0812/diffusionad","url":"https://github.com/huizhang0812/diffusionad"},{"title":"HuiZhang0812/DiffusionAD-Denoising-Diffusion-for-Anomaly-Detection","url":"https://github.com/HuiZhang0812/DiffusionAD-Denoising-Diffusion-for-Anomaly-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/diffusionad-denoising-diffusion-for-anomaly","title":"DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection","date":"2023-03-15","rows_on_this_dataset":1,"code_links":2,"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."}