{"url":"/dataset/dcm","name":"DCM","full_name":null,"description_markdown":"The DCM dataset is composed of 772 annotated images from 27 golden age comic books. We freely collected them from the free public domain collection of digitized comic books [Digital Comics Museum](http://digitalcomicmuseum.com/). One album per available publisher was selected to get as many different styles as possible. We made ground-truth bounding boxes of all panels, all characters (body + faces), small or big, human-like or animal-like.\r\n\r\nImage source: [https://gitlab.univ-lr.fr/crigau02/dcm-dataset/-/tree/master](https://gitlab.univ-lr.fr/crigau02/dcm-dataset/-/tree/master)","description_withheld":null,"homepage":"https://gitlab.univ-lr.fr/crigau02/dcm-dataset/-/tree/master","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Face Detection","url":"/task/face-detection","datasets_with_task":"/datasets/task/face-detection"},{"name":"Body Detection","url":"/task/body-detection","datasets_with_task":"/datasets/task/body-detection"}],"languages":[],"variants":["DCM"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/depth-estimation-on-dcm","task":"Depth Estimation","dataset_variant":"DCM","rows":3,"metrics":["Abs Rel","RMSE","RMSE log","Sq Rel"],"first_row_in_archive_order":{"model":"Bhattacharjee et al.","paper":"/paper/estimating-image-depth-in-the-comics-domain","metrics":{"Abs Rel":"0.251","RMSE":"0.971","RMSE log":"0.305","Sq Rel":"0.318"},"code_links":[{"title":"IVRL/ComicsDepth","url":"https://github.com/IVRL/ComicsDepth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/body-detection-on-dcm","task":"Body Detection","dataset_variant":"DCM","rows":2,"metrics":["Average Precision"],"first_row_in_archive_order":{"model":"DASS-Detector (YOLOX XL)","paper":"/paper/domain-adaptive-self-supervised-pre-training","metrics":{"Average Precision":"86.14"},"code_links":[{"title":"barisbatuhan/dass_det_inference","url":"https://github.com/barisbatuhan/dass_det_inference"},{"title":"barisbatuhan/dass_detector","url":"https://github.com/barisbatuhan/dass_detector"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-detection-on-dcm","task":"Face Detection","dataset_variant":"DCM","rows":2,"metrics":["Average Precision"],"first_row_in_archive_order":{"model":"DASS-Detector (YOLOX XL)","paper":"/paper/domain-adaptive-self-supervised-pre-training","metrics":{"Average Precision":"77.40"},"code_links":[{"title":"barisbatuhan/dass_det_inference","url":"https://github.com/barisbatuhan/dass_det_inference"},{"title":"barisbatuhan/dass_detector","url":"https://github.com/barisbatuhan/dass_detector"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/domain-adaptive-self-supervised-pre-training","title":"Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings","date":"2022-11-19","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/estimating-image-depth-in-the-comics-domain","title":"Estimating Image Depth in the Comics Domain","date":"2021-10-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-robust-monocular-depth-estimation","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","date":"2019-07-02","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":17,"samples_ran":13,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/t2net-synthetic-to-realistic-translation-for","title":"T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks","date":"2018-08-04","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":17,"samples_ran":13,"samples_unverified":4,"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."}