{"url":"/dataset/jhu-crowd-1","name":"JHU-CROWD++","full_name":null,"description_markdown":"JHU-CROWD++ is A large-scale unconstrained crowd counting dataset with 4,372 images and 1.51 million annotations. This dataset is collected under a variety of diverse scenarios and environmental conditions. In addition, the dataset provides comparatively richer set of annotations like dots, approximate bounding boxes, blur levels, etc.","description_withheld":null,"homepage":"http://www.crowd-counting.com/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/jhu-crowd-large-scale-crowd-counting-dataset","title":"JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method","first_author":"Vishwanath A. Sindagi","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Density Estimation","url":"/task/density-estimation","datasets_with_task":"/datasets/task/density-estimation"},{"name":"Crowd Counting","url":"/task/crowd-counting","datasets_with_task":"/datasets/task/crowd-counting"}],"languages":[],"variants":["JHU-CROWD++"],"data_loaders":[],"num_papers_in_archive":48,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/crowd-counting-on-jhu-crowd","task":"Crowd Counting","dataset_variant":"JHU-CROWD++","rows":3,"metrics":["MAE","MSE","MSE "],"first_row_in_archive_order":{"model":"EffCC-Lite0.5","paper":"/paper/improved-knowledge-distillation-for-crowd","metrics":{"MAE":"77.24","MSE ":"276.17"},"code_links":[{"title":"huangzuo/effcc_distilled","url":"https://github.com/huangzuo/effcc_distilled"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/improving-point-based-crowd-counting-and","title":"Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance","date":"2024-05-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improved-knowledge-distillation-for-crowd","title":"Improved Knowledge Distillation for Crowd Counting on IoT Device","date":"2023-08-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-spatial-invariance-of-1","title":"Rethinking Spatial Invariance of Convolutional Networks for Object Counting","date":"2022-06-10","rows_on_this_dataset":1,"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."}