{"url":"/sota/small-object-detection-on-sod4sb-public-test-1","task":{"name":"Small Object Detection","url":"/task/small-object-detection","note":null},"dataset":{"name":"SOD4SB Public Test","url":"/dataset/sod4sb"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Small Object Detection** is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Feature-Fused SSD](https://arxiv.org/pdf/1709.05054v3.pdf) )</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":["AP50"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AP50":null}},"counts":{"rows":5,"rows_with_code":3,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":5},"rows":[{"rank_in_archive_order":1,"model":"Weighted Box Fusion (WBF)","metrics":{"AP50":"77.6"},"uses_additional_data":true,"paper_date":"2023-08-22","paper":"/paper/ensemble-fusion-for-small-object-detection","paper_url":"https://ieeexplore.ieee.org/document/10215748","paper_title":"Ensemble Fusion for Small Object Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"GFL + Test Time Augmentation","metrics":{"AP50":"73.1"},"uses_additional_data":true,"paper_date":"2023-07-21","paper":"/paper/bandre-rethinking-band-pass-filters-for-scale","paper_url":"https://arxiv.org/abs/2307.11748v1","paper_title":"BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation","code":"https://github.com/shinya7y/UniverseNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"DL method (YOLOv8 + Ensamble)","metrics":{"AP50":"73.1"},"uses_additional_data":true,"paper_date":"2023-07-18","paper":"/paper/mva2023-small-object-detection-challenge-for","paper_url":"https://arxiv.org/abs/2307.09143v1","paper_title":"MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results","code":"https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Swin Transformer + Hierarchical design","metrics":{"AP50":"70.2"},"uses_additional_data":true,"paper_date":"2023-08-22","paper":"/paper/small-object-detection-for-birds-with-swin","paper_url":"https://ieeexplore.ieee.org/document/10216093","paper_title":"Small Object Detection for Birds with Swin Transformer","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet)","metrics":{"AP50":"69.6"},"uses_additional_data":true,"paper_date":"2023-07-18","paper":"/paper/mva2023-small-object-detection-challenge-for","paper_url":"https://arxiv.org/abs/2307.09143v1","paper_title":"MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results","code":"https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}