{"url":"/dataset/cod10k","name":"COD10K","full_name":"Camouflaged/Concealed Object Detection","description_markdown":"Sensory ecologists have found that this s background matching camouflage strategy works by deceiving the visual perceptual system of the observer. Naturally, addressing concealed object detection (COD) requires a significant amount of visual perception knowledge. Understanding COD has not only scientific value in itself, but it also important for applications in many fundamental fields, such as computer vision (e.g., for search-and-rescue work, or rare species discovery), medicine (e.g., polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection. Although it has gained increased attention recently, studies on COD still remain scarce, mainly due to the lack of a sufficiently large dataset and a standard benchmark like Pascal-VOC, ImageNet, MS-COCO, ADE20K, and DAVIS.\r\n\r\nTo build the large-scale COD dataset, we build the COD10K, which contains 10,000 images (5,066 camouflaged, 3,000 background, 1,934 noncamouflaged), divided into 10 super-classes, and 78 sub-classes (69 camouflaged, nine non-camouflaged) which are collected from multiple photography websites.","description_withheld":null,"homepage":"https://dengpingfan.github.io/pages/COD.html","introduced_date":"2020-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/camouflaged-object-detection","title":"Camouflaged Object Detection","first_author":"Deng-Ping Fan","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"},{"name":"Camouflaged Object Segmentation with a Single Task-generic Prompt","url":"/task/camouflaged-object-segmentation-with-a-single","datasets_with_task":"/datasets/task/camouflaged-object-segmentation-with-a-single"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["COD","COD10K"],"data_loaders":[],"num_papers_in_archive":166,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/camouflaged-object-segmentation-on-cod","task":"Camouflaged Object Segmentation","dataset_variant":"COD","rows":12,"metrics":["S-Measure","Weighted F-Measure","MAE"],"first_row_in_archive_order":{"model":"BiRefNet","paper":"/paper/bilateral-reference-for-high-resolution","metrics":{"MAE":"0.014","S-Measure":"0.913","Weighted F-Measure":"0.874"},"code_links":[{"title":"zhengpeng7/birefnet","url":"https://github.com/zhengpeng7/birefnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/camouflaged-object-segmentation-with-a-single-2","task":"Camouflaged Object Segmentation with a Single Task-generic Prompt","dataset_variant":"COD10K","rows":3,"metrics":["E_{\\phi}","F_{\\beta}","MAE","S_{\\alpha}"],"first_row_in_archive_order":{"model":"RDVP-MSD","paper":null,"metrics":{"E_{\\phi}":"0.877","MAE":"0.038","S_{\\alpha}":"0.825"},"code_links":[]},"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/leveraging-hallucinations-to-reduce-manual","title":"Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation","date":"2024-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/relax-image-specific-prompt-requirement-in","title":"Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects","date":"2023-12-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":8,"samples_unverified":5,"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/explicit-visual-prompting-for-universal","title":"Explicit Visual Prompting for Universal Foreground Segmentations","date":"2023-05-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/explicit-visual-prompting-for-low-level","title":"Explicit Visual Prompting for Low-Level Structure Segmentations","date":"2023-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"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."}},{"paper":"/paper/camouflaged-object-detection","title":"Camouflaged Object Detection","date":"2020-06-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/egnet-edge-guidance-network-for-salient","title":"EGNet: Edge Guidance Network for Salient Object Detection","date":"2019-10-01","rows_on_this_dataset":1,"code_links":7,"syntology":null},{"paper":"/paper/basnet-boundary-aware-salient-object","title":"BASNet: Boundary-Aware Salient Object Detection","date":"2019-06-01","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/cascaded-partial-decoder-for-fast-and","title":"Cascaded Partial Decoder for Fast and Accurate Salient Object Detection","date":"2019-04-18","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":54,"samples_ran":38,"samples_unverified":16,"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."}