{"url":"/sota/real-time-semantic-segmentation-on-flame","task":{"name":"Real-Time Semantic Segmentation","url":"/task/real-time-semantic-segmentation","note":null},"dataset":{"name":"FLAME","url":"/dataset/flame"},"category":"Computer Vision","categories":["Computer Code","Computer Vision","Medical","Robots"],"category_note":null,"description":"Semantic Segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. In **Real-Time Semantic Segmentation**, the goal is to perform this labeling quickly and accurately in real-time, allowing for the segmentation results to be used for tasks such as object recognition, scene understanding, and autonomous navigation.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [TorchSeg](https://github.com/ycszen/TorchSeg) )</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":["FPS","Mean Intersection over Union","Mean Pixel Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"FPS":null,"Mean Intersection over Union":null,"Mean Pixel Accuracy":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Fast DeepLabV3+","metrics":{"FPS":"59","Mean Intersection over Union":"86.98","Mean Pixel Accuracy":"92.46"},"uses_additional_data":false,"paper_date":"2022-07-22","paper":"/paper/a-real-time-fire-segmentation-method-based-on","paper_url":"https://www.sciencedirect.com/science/article/pii/S2405896322005055","paper_title":"A Real-time Fire Segmentation Method Based on A Deep Learning Approach","code":"https://github.com/maidacundo/real-time-fire-segmentation-deep-learning","n_code_links":1,"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":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"}}}