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A total of 6,637 temporal annotations are automatically\nparsed from online match reports at a one minute resolution for three main\nclasses of events (Goal, Yellow/Red Card, and Substitution). As such, the\ndataset is easily scalable. These annotations are manually refined to a one\nsecond resolution by anchoring them at a single timestamp following\nwell-defined soccer rules. With an average of one event every 6.9 minutes, this\ndataset focuses on the problem of localizing very sparse events within long\nvideos. We define the task of spotting as finding the anchors of soccer events\nin a video. Making use of recent developments in the realm of generic action\nrecognition and detection in video, we provide strong baselines for detecting\nsoccer events. We show that our best model for classifying temporal segments of\nlength one minute reaches a mean Average Precision (mAP) of 67.8%. For the\nspotting task, our baseline reaches an Average-mAP of 49.7% for tolerances\n$\\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at\nhttps://silviogiancola.github.io/SoccerNet.","url_abs":"http://arxiv.org/abs/1804.04527v2","url_pdf":"http://arxiv.org/pdf/1804.04527v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"soccernet-a-scalable-dataset-for-action","repo_url":"https://github.com/cioppaanthony/context-aware-loss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"soccernet-a-scalable-dataset-for-action","repo_url":"https://github.com/SilvioGiancola/SoccerNet-code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-spotting","task_name":"Action Spotting"}],"methods":[],"datasets_introduced":[{"slug":"soccernet","name":"SoccerNet","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-spotting-on-soccernet","task":"Action Spotting","dataset":"SoccerNet","model":"NetVLAD (Giancola et al.)","rank_in_archive_order":6,"of":7,"metrics":{"Average-mAP":"49.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.04527","atlas_url":"https://app.syntology.ai/?focus=1804.04527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.04527"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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