{"url":"/dataset/fbms","name":"FBMS","full_name":"Freiburg-Berkeley Motion Segmentation","description_markdown":"The **Freiburg-Berkeley Motion Segmentation** Dataset (**FBMS**-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set and a test set.\r\n\r\nSource: [https://lmb.informatik.uni-freiburg.de/resources/datasets/](https://lmb.informatik.uni-freiburg.de/resources/datasets/)\r\nImage Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/](https://lmb.informatik.uni-freiburg.de/resources/datasets/)","description_withheld":null,"homepage":"https://lmb.informatik.uni-freiburg.de/resources/datasets/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Segmentation of Moving Objects by Long Term Video Analysis","first_author":null,"url":"https://doi.org/10.1109/TPAMI.2013.242"},"license":{"name":"Custom (research-only, non-commercial)","url":"https://lmb.informatik.uni-freiburg.de/resources/datasets/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Object Segmentation","url":"/task/video-object-segmentation","datasets_with_task":"/datasets/task/video-object-segmentation"},{"name":"Video Salient Object Detection","url":"/task/video-salient-object-detection","datasets_with_task":"/datasets/task/video-salient-object-detection"},{"name":"Unsupervised Object Segmentation","url":"/task/unsupervised-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-object-segmentation"},{"name":"Unsupervised Video Object Segmentation","url":"/task/unsupervised-video-object-segmentation","datasets_with_task":"/datasets/task/unsupervised-video-object-segmentation"}],"languages":[],"variants":["FBMS-59","FBMS"],"data_loaders":[],"num_papers_in_archive":126,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-object-segmentation-on-fbms","task":"Video Object Segmentation","dataset_variant":"FBMS","rows":2,"metrics":["F-Score","Jaccard (Mean)"],"first_row_in_archive_order":{"model":"DFNet","paper":"/paper/learning-discriminative-feature-with-crf-for","metrics":{"F-Score":"82.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-source-fusion-and-automatic-predictor","title":"Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation","date":"2021-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-discriminative-feature-with-crf-for","title":"Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation","date":"2020-08-04","rows_on_this_dataset":1,"code_links":0,"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."}