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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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소개
한국어
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  1. 홈
  2. SOTA
  3. 세마틱 세그멘테이션
  4. Semantic Segmentation On Isprs Vaihingen

Semantic Segmentation On Isprs Vaihingen

평가 지표

Average F1
Overall Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Average F1
Overall Accuracy
Paper TitleRepository
EfficientUNets and Transformers93.791.8Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers-
UNetFormer90.491.0UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
ABCNet-90.7ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
LSKNet-T91.793.6LSKNet: A Foundation Lightweight Backbone for Remote Sensing
BANet-90.5Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
SFA-Net91.2-SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation-
LSKNet-S91.893.6LSKNet: A Foundation Lightweight Backbone for Remote Sensing
DC-Swin90.791.6A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
FT-UNetFormer91.391.6UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
UPerNet (SAP)-90.14Stochastic Subsampling With Average Pooling-
MANet-90.963Multiattention network for semantic segmentation of fine-resolution remote sensing images-
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뉴스튜토리얼데이터셋백과사전

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