{"url":"/dataset/wisdom","name":"WISDOM","full_name":"Warehouse Instance Segmentation Dataset for Object Manipulation","description_markdown":"Synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models. \r\n\r\nSource: [Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data](/paper/segmenting-unknown-3d-objects-from-real-depth)","description_withheld":null,"homepage":"https://github.com/BerkeleyAutomation/sd-maskrcnn","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/segmenting-unknown-3d-objects-from-real-depth","title":"Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data","first_author":"Michael Danielczuk","url":null},"license":null,"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Unseen Object Instance Segmentation","url":"/task/unseen-object-instance-segmentation","datasets_with_task":"/datasets/task/unseen-object-instance-segmentation"}],"languages":[],"variants":["WISDOM"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/wisdom-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/BerkeleyAutomation/sd-maskrcnn","url":"https://github.com/BerkeleyAutomation/sd-maskrcnn","frameworks":[]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unseen-object-instance-segmentation-on-wisdom","task":"Unseen Object Instance Segmentation","dataset_variant":"WISDOM","rows":1,"metrics":["mAP @0.5:0.95"],"first_row_in_archive_order":{"model":"SD Mask R-CNN","paper":"/paper/segmenting-unknown-3d-objects-from-real-depth","metrics":{"mAP @0.5:0.95":"51.6"},"code_links":[{"title":"BerkeleyAutomation/sd-maskrcnn","url":"https://github.com/BerkeleyAutomation/sd-maskrcnn"},{"title":"2024-MindSpore-1/Code4","url":"https://github.com/2024-MindSpore-1/Code4/tree/main/FasterRCNN"},{"title":"BugBoy13/sd-maskrcnn","url":"https://github.com/BugBoy13/sd-maskrcnn"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/MaskRCNN/maskrcnn_mobilenetv1"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segmenting-unknown-3d-objects-from-real-depth","title":"Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data","date":"2018-09-16","rows_on_this_dataset":1,"code_links":4,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}