{"url":"/dataset/camo","name":"CAMO","full_name":"Camouflaged Object","description_markdown":"Camouflaged Object (CAMO) dataset specifically designed for the task of camouflaged object segmentation. We focus on two categories, i.e., naturally camouflaged objects and artificially camouflaged objects, which usually correspond to animals and humans in the real world, respectively. Camouflaged object images consists of 1250 images (1000 images for the training set and 250 images for the testing set). Non-camouflaged object images are collected from the MS-COCO dataset (1000 images for the training set and 250 images for the testing set). CAMO has objectness mask ground-truth.","description_withheld":null,"homepage":"https://sites.google.com/view/ltnghia/research/camo","introduced_date":"2021-05-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/anabranch-network-for-camouflaged-object-1","title":"Anabranch Network for Camouflaged Object Segmentation","first_author":"Trung-Nghia Le","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":["CAMO"],"data_loaders":[],"num_papers_in_archive":139,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/camouflaged-object-segmentation-on-camo","task":"Camouflaged Object Segmentation","dataset_variant":"CAMO","rows":14,"metrics":["S-Measure","Weighted F-Measure","MAE"],"first_row_in_archive_order":{"model":"FOCUS","paper":"/paper/focus-towards-universal-foreground","metrics":{"MAE":"0.025","S-Measure":"0.912","Weighted F-Measure":"0.904"},"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"},{"leaderboard":"/sota/camouflaged-object-segmentation-with-a-single-1","task":"Camouflaged Object Segmentation with a Single Task-generic Prompt","dataset_variant":"CAMO","rows":3,"metrics":["E_{\\phi}","F_{\\beta}","MAE","S_{\\alpha}"],"first_row_in_archive_order":{"model":"RDVP-MSD","paper":null,"metrics":{"E_{\\phi}":"0.848","MAE":"0.081","S_{\\alpha}":"0.796"},"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/improving-existing-segmentators-performance","title":"Improving existing segmentators performance with zero-shot segmentators","date":"2023-07-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/mirrornet-bio-inspired-adversarial-attack-for-1","title":"MirrorNet: Bio-Inspired Camouflaged Object Segmentation","date":"2020-07-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pranet-parallel-reverse-attention-network-for","title":"PraNet: Parallel Reverse Attention Network for Polyp Segmentation","date":"2020-06-13","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"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}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":7,"samples_harvested":63,"samples_ran":38,"samples_unverified":25,"pointer_only_for_licence":11,"papers_with_no_sample_that_ran":1,"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."}