Semantic Segmentation On Selma

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
CMX	91.7	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
SegFormer	77.2	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
DeepLabV3	70.7	Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabV2	68.9	DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
PSPNet	68.4	Pyramid Scene Parsing Network
FCN	68.2	Fully Convolutional Networks for Semantic Segmentation
UNet	36.2	U-Net: Convolutional Networks for Biomedical Image Segmentation

0 of 7 row(s) selected.