{"url":"/dataset/seeds","name":"seeds","full_name":null,"description_markdown":"The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for the experiment. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates. Studies were conducted using combine harvested wheat grain originating from experimental fields, explored at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin.\r\n\r\nThe data set can be used for the tasks of classification and cluster analysis.","description_withheld":null,"homepage":"https://archive.ics.uci.edu/ml/datasets/seeds","introduced_date":"2010-04-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/complete-gradient-clustering-algorithm-for","title":"Complete gradient clustering algorithm for features analysis of x-ray images","first_author":"M. Charytanowicz","url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"Clustering Algorithms Evaluation","url":"/task/clustering-algorithms-evaluation","datasets_with_task":"/datasets/task/clustering-algorithms-evaluation"}],"languages":[],"variants":["seeds"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/uci-seeds-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/clustering-algorithms-evaluation-on-seeds","task":"Clustering Algorithms Evaluation","dataset_variant":"seeds","rows":1,"metrics":["Purity"],"first_row_in_archive_order":{"model":"CVDD","paper":"/paper/an-internal-validity-index-based-on-density","metrics":{"Purity":"0.905"},"code_links":[{"title":"hulianyu/CVDD","url":"https://github.com/hulianyu/CVDD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/an-internal-validity-index-based-on-density","title":"An Internal Validity Index Based on Density-Involved Distance","date":"2019-03-22","rows_on_this_dataset":1,"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."}