{"url":"/sota/crowd-counting-on-trancos","task":{"name":"Crowd Counting","url":"/task/crowd-counting","note":null},"dataset":{"name":"TRANCOS","url":"/dataset/trancos"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Crowd Counting** is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.\r\n\r\n\r\n<span class=\"description-source\">Source: [Deep Density-aware Count Regressor ](https://arxiv.org/abs/1908.03314)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["MAE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"MAE":"lower"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"M-SFANet+M-SegNet","metrics":{"MAE":"2.22"},"uses_additional_data":false,"paper_date":"2020-03-12","paper":"/paper/encoder-decoder-based-convolutional-neural","paper_url":"https://arxiv.org/abs/2003.05586v5","paper_title":"Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting","code":"https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"ADCrowdNet","metrics":{"MAE":"2.44"},"uses_additional_data":false,"paper_date":"2018-11-29","paper":"/paper/adcrowdnet-an-attention-injective-deformable","paper_url":"http://arxiv.org/abs/1811.11968v5","paper_title":"ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding","code":"https://github.com/BIGKnight/ADCrowd_pytorch_implementation","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"S-DCNet","metrics":{"MAE":"2.92"},"uses_additional_data":false,"paper_date":"2019-08-15","paper":"/paper/from-open-set-to-closed-set-counting-objects","paper_url":"https://arxiv.org/abs/1908.06473v1","paper_title":"From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer","code":"https://github.com/xhp-hust-2018-2011/S-DCNet","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":5,"n_samples":8,"n_pointer_only_licence":3}},{"rank_in_archive_order":4,"model":"CSRNet","metrics":{"MAE":"3.56"},"uses_additional_data":false,"paper_date":"2018-02-27","paper":"/paper/csrnet-dilated-convolutional-neural-networks","paper_url":"http://arxiv.org/abs/1802.10062v4","paper_title":"CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes","code":"https://github.com/leeyeehoo/CSRNet-pytorch","n_code_links":11,"syntology":{"n_ran":5,"n_unverified":10,"n_samples":15,"n_pointer_only_licence":5}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":8,"n_unverified":15,"n_samples":23,"n_pointer_only_licence":8,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":8,"n_unverified":15,"n_samples":23,"n_pointer_only_licence":8,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}