{"url":"/sota/semantic-segmentation-on-isaid","task":{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","note":null},"dataset":{"name":"iSAID","url":"/dataset/isaid"},"category":"Computer Vision","categories":["Computer Code","Computer Vision","Medical","Robots"],"category_note":null,"description":null,"description_from":null,"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":["mIoU"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mIoU":null}},"counts":{"rows":19,"rows_with_code":18,"rows_with_paper_page":19,"rows_dated":19,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SegNeXt-L","metrics":{"mIoU":"70.3"},"uses_additional_data":false,"paper_date":"2022-09-18","paper":"/paper/segnext-rethinking-convolutional-attention","paper_url":"https://arxiv.org/abs/2209.08575v1","paper_title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","code":"https://github.com/open-mmlab/mmsegmentation","n_code_links":5,"syntology":null},{"rank_in_archive_order":2,"model":"SegNeXt-B","metrics":{"mIoU":"69.9"},"uses_additional_data":false,"paper_date":"2022-09-18","paper":"/paper/segnext-rethinking-convolutional-attention","paper_url":"https://arxiv.org/abs/2209.08575v1","paper_title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","code":"https://github.com/open-mmlab/mmsegmentation","n_code_links":5,"syntology":null},{"rank_in_archive_order":3,"model":"AerialFormer-B","metrics":{"mIoU":"69.3"},"uses_additional_data":false,"paper_date":"2023-06-12","paper":"/paper/aerialformer-multi-resolution-transformer-for","paper_url":"https://arxiv.org/abs/2306.06842v2","paper_title":"AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation","code":"https://github.com/UARK-AICV/AerialFormer","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"SegNeXt-S","metrics":{"mIoU":"68.8"},"uses_additional_data":false,"paper_date":"2022-09-18","paper":"/paper/segnext-rethinking-convolutional-attention","paper_url":"https://arxiv.org/abs/2209.08575v1","paper_title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","code":"https://github.com/open-mmlab/mmsegmentation","n_code_links":5,"syntology":null},{"rank_in_archive_order":5,"model":"AerialFormer-S","metrics":{"mIoU":"68.4"},"uses_additional_data":false,"paper_date":"2023-06-12","paper":"/paper/aerialformer-multi-resolution-transformer-for","paper_url":"https://arxiv.org/abs/2306.06842v2","paper_title":"AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation","code":"https://github.com/UARK-AICV/AerialFormer","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"SegNeXt-T","metrics":{"mIoU":"68.3"},"uses_additional_data":false,"paper_date":"2022-09-18","paper":"/paper/segnext-rethinking-convolutional-attention","paper_url":"https://arxiv.org/abs/2209.08575v1","paper_title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","code":"https://github.com/open-mmlab/mmsegmentation","n_code_links":5,"syntology":null},{"rank_in_archive_order":7,"model":"FarSeg++@MiT-B2","metrics":{"mIoU":"67.9"},"uses_additional_data":false,"paper_date":"2023-07-13","paper":"/paper/farseg-foreground-aware-relation-network-for","paper_url":"https://ieeexplore.ieee.org/document/10188509","paper_title":"FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery","code":"https://github.com/Z-Zheng/FarSeg","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"FarSeg++@ResNet-50","metrics":{"mIoU":"67.6"},"uses_additional_data":false,"paper_date":"2023-07-13","paper":"/paper/farseg-foreground-aware-relation-network-for","paper_url":"https://ieeexplore.ieee.org/document/10188509","paper_title":"FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery","code":"https://github.com/Z-Zheng/FarSeg","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"AerialFormer-T","metrics":{"mIoU":"67.5"},"uses_additional_data":false,"paper_date":"2023-06-12","paper":"/paper/aerialformer-multi-resolution-transformer-for","paper_url":"https://arxiv.org/abs/2306.06842v2","paper_title":"AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation","code":"https://github.com/UARK-AICV/AerialFormer","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"DeepLabV3 with R-50","metrics":{"mIoU":"67.03"},"uses_additional_data":false,"paper_date":"2023-07-26","paper":"/paper/resolution-aware-design-of-atrous-rates-for","paper_url":"https://arxiv.org/abs/2307.14179v1","paper_title":"Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"FarSeg++@Swin-T","metrics":{"mIoU":"66.3"},"uses_additional_data":false,"paper_date":"2023-07-13","paper":"/paper/farseg-foreground-aware-relation-network-for","paper_url":"https://ieeexplore.ieee.org/document/10188509","paper_title":"FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery","code":"https://github.com/Z-Zheng/FarSeg","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"IMP-ViTAEv2-S-UperNet","metrics":{"mIoU":"65.3"},"uses_additional_data":false,"paper_date":"2022-04-06","paper":"/paper/an-empirical-study-of-remote-sensing","paper_url":"https://arxiv.org/abs/2204.02825v4","paper_title":"An Empirical Study of Remote Sensing Pretraining","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":null},{"rank_in_archive_order":13,"model":"FactSeg@ResNet-50","metrics":{"mIoU":"64.79"},"uses_additional_data":false,"paper_date":"2021-07-27","paper":"/paper/factseg-foreground-activation-driven-small","paper_url":"https://ieeexplore.ieee.org/document/9497514","paper_title":"FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery","code":"https://github.com/Junjue-Wang/FactSeg","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"ViTAE-B + RVSA-UperNet","metrics":{"mIoU":"64.49"},"uses_additional_data":false,"paper_date":"2022-08-08","paper":"/paper/advancing-plain-vision-transformer-towards","paper_url":"https://arxiv.org/abs/2208.03987v4","paper_title":"Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":15,"model":"RSP-ViTAEv2-S-UperNet","metrics":{"mIoU":"64.3"},"uses_additional_data":false,"paper_date":"2022-04-06","paper":"/paper/an-empirical-study-of-remote-sensing","paper_url":"https://arxiv.org/abs/2204.02825v4","paper_title":"An Empirical Study of Remote Sensing Pretraining","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":null},{"rank_in_archive_order":16,"model":"RSP-Swin-T-UperNet","metrics":{"mIoU":"64.1"},"uses_additional_data":false,"paper_date":"2022-04-06","paper":"/paper/an-empirical-study-of-remote-sensing","paper_url":"https://arxiv.org/abs/2204.02825v4","paper_title":"An Empirical Study of Remote Sensing Pretraining","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":null},{"rank_in_archive_order":17,"model":"ViT-B + RVSA-UperNet","metrics":{"mIoU":"63.85"},"uses_additional_data":false,"paper_date":"2022-08-08","paper":"/paper/advancing-plain-vision-transformer-towards","paper_url":"https://arxiv.org/abs/2208.03987v4","paper_title":"Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":1,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"FarSeg@ResNet-50","metrics":{"mIoU":"63.71"},"uses_additional_data":false,"paper_date":"2020-11-19","paper":"/paper/foreground-aware-relation-network-for-1","paper_url":"https://arxiv.org/abs/2011.09766v1","paper_title":"Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery","code":"https://github.com/PaddlePaddle/PaddleRS","n_code_links":2,"syntology":null},{"rank_in_archive_order":19,"model":"RSP-ResNet-50-UperNet","metrics":{"mIoU":"61.6"},"uses_additional_data":false,"paper_date":"2022-04-06","paper":"/paper/an-empirical-study-of-remote-sensing","paper_url":"https://arxiv.org/abs/2204.02825v4","paper_title":"An Empirical Study of Remote Sensing Pretraining","code":"https://github.com/vitae-transformer/vitae-transformer-remote-sensing","n_code_links":2,"syntology":null}],"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":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":1,"n_samples":4,"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":6,"n_unverified":2,"n_samples":8,"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"}}}