{"url":"/dataset/itdd","name":"ITDD","full_name":"Industrial Textile Defect Detection","description_markdown":"The Industrial Textile Defect Detection (ITDD) dataset includes 1885 industrial textile images categorized into 4 categories: cotton fabric, dyed fabric, hemp fabric, and plaid fabric. These classes are collected from the industrial production sites of WEIQIAO Textile. ITDD is an upgraded version of WFDD that reorganizes three original classes and adds one new class.","description_withheld":null,"homepage":"https://github.com/cqylunlun/CRAS?tab=readme-ov-file#dataset-release","introduced_date":"2025-05-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/center-aware-residual-anomaly-synthesis-for","title":"Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection","first_author":"Qiyu Chen","url":null},"license":{"name":"MIT license","url":"https://github.com/cqylunlun/CRAS?tab=MIT-1-ov-file"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Multi-class Anomaly Detection","url":"/task/multi-class-anomaly-detection","datasets_with_task":"/datasets/task/multi-class-anomaly-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ITDD"],"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/anomaly-detection-on-itdd","task":"Anomaly Detection","dataset_variant":"ITDD","rows":1,"metrics":["Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"CRAS","paper":"/paper/center-aware-residual-anomaly-synthesis-for","metrics":{"Detection AUROC":"99.6","Segmentation AUROC":"98.0"},"code_links":[{"title":"cqylunlun/CRAS","url":"https://github.com/cqylunlun/CRAS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-class-anomaly-detection-on-itdd","task":"Multi-class Anomaly Detection","dataset_variant":"ITDD","rows":1,"metrics":["Detection AUROC","Segmentation AUROC"],"first_row_in_archive_order":{"model":"CRAS","paper":"/paper/center-aware-residual-anomaly-synthesis-for","metrics":{"Detection AUROC":"99.4","Segmentation AUROC":"97.8"},"code_links":[{"title":"cqylunlun/CRAS","url":"https://github.com/cqylunlun/CRAS"}]},"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}],"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."}