{"url":"/dataset/nwpu-crowd","name":"NWPU-Crowd","full_name":null,"description_markdown":"NWPU-Crowd consists of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033). \r\n\r\nSource: [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](/paper/nwpu-crowd-a-large-scale-benchmark-for-crowd)","description_withheld":null,"homepage":"https://gjy3035.github.io/NWPU-Crowd-Sample-Code/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/nwpu-crowd-a-large-scale-benchmark-for-crowd","title":"NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization","first_author":"Qi. Wang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Speech Recognition","url":"/task/speech-recognition","datasets_with_task":"/datasets/task/speech-recognition"},{"name":"Crowd Counting","url":"/task/crowd-counting","datasets_with_task":"/datasets/task/crowd-counting"}],"languages":[],"variants":["NWPU-Crowd","NWPU-Crowd (Val)"],"data_loaders":[],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/crowd-counting-on-nwpu-crowd-val","task":"Crowd Counting","dataset_variant":"NWPU-Crowd (Val)","rows":6,"metrics":["MAE","RMSE"],"first_row_in_archive_order":{"model":"EBC-ZIP-B","paper":"/paper/ebc-zip-improving-blockwise-crowd-counting","metrics":{"MAE":"28.26","RMSE":"64.84"},"code_links":[{"title":"yiming-m/ebc-zip","url":"https://github.com/yiming-m/ebc-zip"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/crowd-counting-on-nwpu-crowd","task":"Crowd Counting","dataset_variant":"NWPU-Crowd","rows":1,"metrics":["MAE","MSE"],"first_row_in_archive_order":{"model":"APGCC","paper":"/paper/improving-point-based-crowd-counting-and","metrics":{"MAE":"71.7","MSE":"284.4"},"code_links":[{"title":"AaronCIH/APGCC","url":"https://github.com/AaronCIH/APGCC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ebc-zip-improving-blockwise-crowd-counting","title":"EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson Regression","date":"2025-06-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/clip-ebc-clip-can-count-accurately-through","title":"CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification","date":"2024-03-14","rows_on_this_dataset":5,"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."}