{"url":"/dataset/nc4k","name":"NC4K","full_name":null,"description_markdown":"As far as we know, there only exists one large camouflaged object testing dataset, the COD10K, while the sizes of other testing datasets are less than 300. We then contribute another camouflaged object testing dataset, namely NC4K, which includes 4,121 images downloaded from the Internet. The new testing dataset can be used to evaluate the generalization ability of existing models.","description_withheld":null,"homepage":"https://github.com/JingZhang617/COD-Rank-Localize-and-Segment","introduced_date":"2021-03-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/simultaneously-localize-segment-and-rank-the","title":"Simultaneously Localize, Segment and Rank the Camouflaged Objects","first_author":"Yunqiu Lv","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Camouflaged Object Segmentation","url":"/task/camouflaged-object-segmentation","datasets_with_task":"/datasets/task/camouflaged-object-segmentation"}],"languages":[],"variants":["NC4K"],"data_loaders":[],"num_papers_in_archive":94,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/camouflaged-object-segmentation-on-nc4k","task":"Camouflaged Object Segmentation","dataset_variant":"NC4K","rows":6,"metrics":["S-measure","weighted F-measure","MAE"],"first_row_in_archive_order":{"model":"FOCUS","paper":"/paper/focus-towards-universal-foreground","metrics":{"MAE":"0.020","S-measure":"0.915","weighted F-measure":"0.906"},"code_links":[{"title":"geshang777/FOCUS","url":"https://github.com/geshang777/FOCUS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/focus-towards-universal-foreground","title":"FOCUS: Towards Universal Foreground Segmentation","date":"2025-01-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bilateral-reference-for-high-resolution","title":"Bilateral Reference for High-Resolution Dichotomous Image Segmentation","date":"2024-01-07","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":13,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/zoomnext-a-unified-collaborative-pyramid","title":"ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection","date":"2023-10-31","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/concealed-object-detection","title":"Concealed Object Detection","date":"2021-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":28,"samples_ran":24,"samples_unverified":4,"pointer_only_for_licence":11,"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."}