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Semantic Segmentation On Vaihingen

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name
mIoU
Paper TitleRepository
CMX82.87CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers-
SegFormer-B075.57SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers-
SA-Gate81.03Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation-
HRNet-4876.75Deep High-Resolution Representation Learning for Visual Recognition-
SegFormer-B276.69SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers-
UnetFormer77.24UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery-
PSPNet76.79Pyramid Scene Parsing Network-
LMFNet-2 (82.49LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing-
V-FuseNet79.56Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks-
SegFormer-B176.92SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers-
DeepLabV3+72.90Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation-
HRNet-1875.90Deep High-Resolution Representation Learning for Visual Recognition-
FPN74.86Feature Pyramid Networks for Object Detection-
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Semantic Segmentation On Vaihingen | SOTA | HyperAI