{"url":"/sota/multi-label-classification-on-openimages-v6","task":{"name":"Multi-Label Classification","url":"/task/multi-label-classification","note":null},"dataset":{"name":"OpenImages-v6","url":"/dataset/openimages-v6"},"category":"Computer Vision","categories":["Computer Vision","Medical","Methodology","Reasoning"],"category_note":null,"description":"**Multi-Label Classification** is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.\r\n\r\n\r\n<span class=\"description-source\">Source: [Deep Learning for Multi-label Classification ](https://arxiv.org/abs/1502.05988)</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":["mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mAP":"higher"}},"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":"TResNet-L","metrics":{"mAP":"87.34"},"uses_additional_data":false,"paper_date":"2021-10-21","paper":"/paper/multi-label-classification-with-partial","paper_url":"https://arxiv.org/abs/2110.10955v1","paper_title":"Multi-label Classification with Partial Annotations using Class-aware Selective Loss","code":"https://github.com/alibaba-miil/partiallabelingcsl","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"TResNet-M","metrics":{"mAP":"86.8"},"uses_additional_data":false,"paper_date":"2021-11-25","paper":"/paper/ml-decoder-scalable-and-versatile","paper_url":"https://arxiv.org/abs/2111.12933v2","paper_title":"ML-Decoder: Scalable and Versatile Classification Head","code":"https://github.com/alibaba-miil/ml_decoder","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"TResNet-M","metrics":{"mAP":"86.72"},"uses_additional_data":false,"paper_date":"2021-10-21","paper":"/paper/multi-label-classification-with-partial","paper_url":"https://arxiv.org/abs/2110.10955v1","paper_title":"Multi-label Classification with Partial Annotations using Class-aware Selective Loss","code":"https://github.com/alibaba-miil/partiallabelingcsl","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"TResNet-L","metrics":{"mAP":"86.3"},"uses_additional_data":false,"paper_date":"2020-09-29","paper":"/paper/asymmetric-loss-for-multi-label","paper_url":"https://arxiv.org/abs/2009.14119v4","paper_title":"Asymmetric Loss For Multi-Label Classification","code":"https://github.com/Alibaba-MIIL/ASL","n_code_links":5,"syntology":{"n_ran":4,"n_unverified":8,"n_samples":12,"n_pointer_only_licence":7}}],"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":6,"n_unverified":11,"n_samples":17,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":6,"n_unverified":11,"n_samples":17,"n_pointer_only_licence":7,"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"}}}