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SOTA
Semantische Segmentierung
Semantic Segmentation On Coco Stuff Test
Semantic Segmentation On Coco Stuff Test
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mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
mIoU
Paper Title
Repository
CAA (ResNet-101)
41.2%
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
SenFormer (Swin-L)
50.1%
Efficient Self-Ensemble for Semantic Segmentation
DRAN(ResNet-101)
41.2%
Scene Segmentation with Dual Relation-aware Attention Network
CCL (ResNet-101)
35.7%
Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation
OCR (ResNet-101)
39.5%
Segmentation Transformer: Object-Contextual Representations for 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
OCR (HRNetV2-W48)
40.5%
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
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
SegViT (ours)
50.3%
SegViT: Semantic Segmentation with Plain Vision Transformers
EMANet
39.9%
Expectation-Maximization Attention Networks for Semantic Segmentation
RSSeg-ViT-L
52.0%
Representation Separation for Semantic Segmentation with Vision Transformers
-
DANet (ResNet-101)
39.7%
Dual Attention Network for Scene Segmentation
SVCNet (ResNet-101)
39.6%
Semantic Correlation Promoted Shape-Variant Context for Segmentation
RSSeg-ViT-L (BEiT pretrain)
52.6%
Representation Separation for Semantic Segmentation with Vision Transformers
-
RefineNet (ResNet-101)
33.6%
RefineNet: Multi-Path Refinement Networks for High-Resolution 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
Asymmetric ALNN
37.2%
Asymmetric Non-local Neural Networks for Semantic Segmentation
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Semantic Segmentation On Coco Stuff Test | SOTA | HyperAI