{"url":"/dataset/ucf-qnrf","name":"UCF-QNRF","full_name":null,"description_markdown":"The **UCF-QNRF** dataset is a crowd counting dataset and it contains large diversity both in scenes, as well as in background types. It consists of 1535 images high-resolution images from Flickr, Web Search and Hajj footage. The number of people (i.e., the count) varies from 50 to 12,000 across images.\r\n\r\nSource: [Understanding the impact of mistakes on background regions in crowd counting](https://arxiv.org/abs/2003.13759)\r\nImage Source: [https://www.crcv.ucf.edu/data/ucf-qnrf/](https://www.crcv.ucf.edu/data/ucf-qnrf/)","description_withheld":null,"homepage":"https://www.crcv.ucf.edu/data/ucf-qnrf/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/composition-loss-for-counting-density-map","title":"Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds","first_author":"Haroon Idrees","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Crowd Counting","url":"/task/crowd-counting","datasets_with_task":"/datasets/task/crowd-counting"}],"languages":[],"variants":["UCF-QNRF"],"data_loaders":[{"repo":"https://github.com/osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob","frameworks":["pytorch"]}],"num_papers_in_archive":176,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/crowd-counting-on-ucf-qnrf","task":"Crowd Counting","dataset_variant":"UCF-QNRF","rows":23,"metrics":["MAE","RMSE","MSE"],"first_row_in_archive_order":{"model":"EBC-ZIP-B","paper":"/paper/ebc-zip-improving-blockwise-crowd-counting","metrics":{"MAE":"69.46","RMSE":"121.88"},"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"}],"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/crowd-counting-and-individual-localization","title":"Crowd Counting and Individual Localization Using Pseudo Square Label","date":"2024-05-13","rows_on_this_dataset":1,"code_links":0,"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":6,"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},{"paper":"/paper/distribution-matching-for-crowd-counting","title":"Distribution Matching for Crowd Counting","date":"2020-09-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/encoder-decoder-based-convolutional-neural","title":"Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting","date":"2020-03-12","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/segmentation-guided-attention-network-for","title":"Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss","date":"2019-11-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/context-aware-crowd-counting","title":"Context-Aware Crowd Counting","date":"2018-11-26","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/composition-loss-for-counting-density-map","title":"Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds","date":"2018-08-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/switching-convolutional-neural-network-for","title":"Switching Convolutional Neural Network for Crowd Counting","date":"2017-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cnn-based-cascaded-multi-task-learning-of","title":"CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting","date":"2017-07-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","rows_on_this_dataset":1,"code_links":146,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":71,"samples_ran":21,"samples_unverified":50,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/single-image-crowd-counting-via-multi-column-1","title":"Single-Image Crowd Counting via Multi-Column Convolutional Neural Network","date":"2016-01-01","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","rows_on_this_dataset":1,"code_links":484,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":377,"samples_ran":242,"samples_unverified":135,"pointer_only_for_licence":187,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","date":"2015-11-02","rows_on_this_dataset":1,"code_links":74,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":44,"samples_ran":9,"samples_unverified":35,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-source-multi-scale-counting-in","title":"Multi-source Multi-scale Counting in Extremely Dense Crowd Images","date":"2013-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":5,"samples_harvested":499,"samples_ran":277,"samples_unverified":222,"pointer_only_for_licence":204,"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."}