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SOTA
Semantic Segmentation
Semantic Segmentation On Coco Stuff Test
Semantic Segmentation On Coco Stuff Test
Metrics
mIoU
Results
Performance results of various models on this benchmark
Columns
Model Name
mIoU
Paper Title
VPNeXt
53.7
VPNeXt -- Rethinking Dense Decoding for Plain Vision Transformer
EVA
53.4%
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
RSSeg-ViT-L (BEiT pretrain)
52.6%
Representation Separation for Semantic Segmentation with Vision Transformers
RSSeg-ViT-L
52.0%
Representation Separation for Semantic Segmentation with Vision Transformers
SegViT (ours)
50.3%
SegViT: Semantic Segmentation with Plain Vision Transformers
SenFormer (Swin-L)
50.1%
Efficient Self-Ensemble for Semantic Segmentation
CAA (Efficientnet-B7)
45.4%
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
HRNetV2 + OCR + RMI (PaddleClas pretrained)
45.2%
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
CAA (ResNet-101)
41.2%
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
DRAN(ResNet-101)
41.2%
Scene Segmentation with Dual Relation-aware Attention Network
OCR (HRNetV2-W48)
40.5%
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
EMANet
39.9%
Expectation-Maximization Attention Networks for Semantic Segmentation
DANet (ResNet-101)
39.7%
Dual Attention Network for Scene Segmentation
SVCNet (ResNet-101)
39.6%
Semantic Correlation Promoted Shape-Variant Context for Segmentation
OCR (ResNet-101)
39.5%
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
Asymmetric ALNN
37.2%
Asymmetric Non-local Neural Networks for Semantic Segmentation
CCL (ResNet-101)
35.7%
Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation
RefineNet (ResNet-101)
33.6%
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
DAG-RNN (VGG-16)
31.2%
DAG-Recurrent Neural Networks For Scene Labeling
FCN (VGG-16)
22.7%
Fully Convolutional Networks for Semantic Segmentation
0 of 20 row(s) selected.
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