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Hyperspectral Semantic Segmentation 벤치마크

Hyperspectral Semantic Segmentation on HyKo2-VIS

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Accuracy

84.1 84.75 85.41 86.06 86.72 2024-09 2026-09 RU-Net — 86.72 (2024-09-17) U-Net — 85.36 (2024-09-17) DeepLabV3+ — 84.1 (2024-09-17) RU-Net — 86.72 (2024-09-17) U-Net — 85.36 (2024-09-17) DeepLabV3+ — 84.1 (2024-09-17) RU-Net — 86.72 (2024-09-17)
RankModel AccuracyAverage AccuracyAverage JaccardAvg. F1 PaperCodeYear
1 RU-Net 86.7268.7958.6469.19 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
2 U-Net 85.3668.1557.3968.55 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
3 DeepLabV3+ 84.1063.0153.2264.90 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
4 RU-Net 86.7268.7958.6469.19 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
5 U-Net 85.3668.1557.3968.55 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
6 DeepLabV3+ 84.1063.0153.2264.90 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
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