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Semantic Segmentation On Pascal Voc 2012

評価指標

Mean IoU

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

Paper Title
DeepLabv3+ (Xception-JFT)89.0%Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
DeepLabv3+ (Xception-65-JFT)89.0%Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
DeepLabv3-JFT86.9%Rethinking Atrous Convolution for Semantic Image Segmentation
CASIA_IVA_SDN86.6%Stacked Deconvolutional Network for Semantic Segmentation
Smooth Network with Channel Attention Block86.2%Learning a Discriminative Feature Network for Semantic Segmentation
SANet (pretraining on COCO dataset)86.1%Squeeze-and-Attention Networks for Semantic Segmentation
HamNet w/o COCO (ResNet-101)85.9%Is Attention Better Than Matrix Decomposition?
EncNet85.9%Context Encoding for Semantic Segmentation
Auto-DeepLab-L85.6%Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
PSPNet85.4%Pyramid Scene Parsing Network
ResNet-38 MS COCO84.9%Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
OCR (HRNetV2-W48)84.5%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
OCR (ResNet-101)84.3%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
Multipath-RefineNet84.2%RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
ShelfNet84.2%ShelfNet for Fast Semantic Segmentation
EANet (ResNet-101)84%Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
Large Kernel Matters83.6%Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
TripleNet83.3%Triply Supervised Decoder Networks for Joint Detection and Segmentation
SANet83.2%Squeeze-and-Attention Networks for Semantic Segmentation
TuSimple83.1%Understanding Convolution for Semantic Segmentation
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