{"url":"/dataset/tju-dhd","name":"TJU-DHD","full_name":null,"description_markdown":"**TJU-DHD** is a high-resolution dataset for object detection and pedestrian detection. The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labelled objects in total with a large variance in scale and appearance.\n\nSource: [https://github.com/tjubiit/TJU-DHD](https://github.com/tjubiit/TJU-DHD)\nImage Source: [https://github.com/tjubiit/TJU-DHD](https://github.com/tjubiit/TJU-DHD)","description_withheld":null,"homepage":"https://github.com/tjubiit/TJU-DHD","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/tju-dhd-a-diverse-high-resolution-dataset-for","title":"TJU-DHD: A Diverse High-Resolution Dataset for Object Detection","first_author":"Yanwei Pang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Pedestrian Detection","url":"/task/pedestrian-detection","datasets_with_task":"/datasets/task/pedestrian-detection"}],"languages":[],"variants":["TJU-DHD","TJU-Ped-campus","TJU-Ped-traffic"],"data_loaders":[{"repo":"https://github.com/tjubiit/TJU-DHD","url":"https://github.com/tjubiit/TJU-DHD","frameworks":[]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pedestrian-detection-on-tju-ped-traffic","task":"Pedestrian Detection","dataset_variant":"TJU-Ped-traffic","rows":6,"metrics":["R (miss rate)","RS (miss rate)","HO (miss rate)","R+HO (miss rate)","ALL (miss rate)"],"first_row_in_archive_order":{"model":"LSFM","paper":"/paper/localized-semantic-feature-mixers-for","metrics":{"HO (miss rate)":"56.2","R (miss rate)":"18.7","RS (miss rate)":"24.9"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/pedestrian-detection-on-tju-ped-campus","task":"Pedestrian Detection","dataset_variant":"TJU-Ped-campus","rows":4,"metrics":["R (miss rate)","RS (miss rate)","HO (miss rate)","R+HO (miss rate)","ALL (miss rate)"],"first_row_in_archive_order":{"model":"EGCL","paper":"/paper/pedestrian-detection-by-exemplar-guided","metrics":{"ALL (miss rate)":"34.87","HO (miss rate)":"65.27","R (miss rate)":"24.84","R+HO (miss rate)":"32.39","RS (miss rate)":"-"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/localized-semantic-feature-mixers-for","title":"Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving","date":"2023-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pedestrian-detection-by-exemplar-guided","title":"Pedestrian Detection by Exemplar-Guided Contrastive Learning","date":"2021-11-17","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/detection-in-crowded-scenes-one-proposal","title":"Detection in Crowded Scenes: One Proposal, Multiple Predictions","date":"2020-03-20","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","rows_on_this_dataset":2,"code_links":87,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":40,"samples_ran":13,"samples_unverified":27,"pointer_only_for_licence":18,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","rows_on_this_dataset":2,"code_links":234,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":11,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","date":"2016-12-09","rows_on_this_dataset":1,"code_links":85,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":51,"samples_ran":16,"samples_unverified":35,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":112,"samples_ran":40,"samples_unverified":72,"pointer_only_for_licence":35,"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."}