{"url":"/dataset/taskonomy","name":"Taskonomy","full_name":null,"description_markdown":"Taskonomy provides a large and high-quality dataset of varied indoor scenes.\r\n\r\n- Complete pixel-level geometric information via aligned meshes.\r\n- Semantic information via knowledge distillation from ImageNet, MS COCO, and MIT Places.\r\n- Globally consistent camera poses. Complete camera intrinsics.\r\n- High-definition images.\r\n- 3x times big as ImageNet.\r\n\r\nSource: [Taskonomy](http://taskonomy.stanford.edu/)\r\nImage Source: [http://taskonomy.stanford.edu/](http://taskonomy.stanford.edu/)","description_withheld":null,"homepage":"http://taskonomy.stanford.edu/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/taskonomy-disentangling-task-transfer","title":"Taskonomy: Disentangling Task Transfer Learning","first_author":"Amir Zamir","url":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":"Surface Normals Estimation","url":"/task/surface-normals-estimation","datasets_with_task":"/datasets/task/surface-normals-estimation"}],"languages":[],"variants":["Taskonomy"],"data_loaders":[],"num_papers_in_archive":147,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/depth-estimation-on-taskonomy","task":"Depth Estimation","dataset_variant":"Taskonomy","rows":1,"metrics":["L1 error"],"first_row_in_archive_order":{"model":"X-TC (Cross-Task Consistency)","paper":"/paper/robust-learning-through-cross-task","metrics":{"L1 error":"1.63"},"code_links":[{"title":"EPFL-VILAB/XTConsistency","url":"https://github.com/EPFL-VILAB/XTConsistency"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/surface-normals-estimation-on-taskonomy","task":"Surface Normals Estimation","dataset_variant":"Taskonomy","rows":1,"metrics":["L1 error"],"first_row_in_archive_order":{"model":"X-TC (Cross-Task Consistency)","paper":"/paper/robust-learning-through-cross-task","metrics":{"L1 error":"4.80"},"code_links":[{"title":"EPFL-VILAB/XTConsistency","url":"https://github.com/EPFL-VILAB/XTConsistency"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/robust-learning-through-cross-task","title":"Robust Learning Through Cross-Task Consistency","date":"2020-06-01","rows_on_this_dataset":2,"code_links":1,"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."}