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SOTA
语义分割
Semantic Segmentation On Pascal Voc 2012 Val
Semantic Segmentation On Pascal Voc 2012 Val
评估指标
mIoU
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
mIoU
Paper Title
Repository
EfficientNet-L2+NAS-FPN (single scale test, with self-training)
90.0%
Rethinking Pre-training and Self-training
TADP
87.11%
Text-image Alignment for Diffusion-based Perception
Eff-B7 NAS-FPN (Copy-Paste pre-training, single-scale))
86.6%
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
ExFuse (ResNeXt-131)
85.8%
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
-
SpineNet-S143 (single-scale test)
85.64%
Dilated SpineNet for Semantic Segmentation
-
DeepLabv3-JFT
82.7%
Rethinking Atrous Convolution for Semantic Image Segmentation
Auto-DeepLab-L
82.04%
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
ResNet-GCN
81.0%
Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
HyperSeg-L
80.61%
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
DFN (ResNet-101)
80.60%
Learning a Discriminative Feature Network for Semantic Segmentation
WASPnet-CRF (ours)
80.41%
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
Deeplab v3+ (Res2Net-101)
79.3%
Res2Net: A New Multi-scale Backbone Architecture
FastDenseNas-arch0
78.0%
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
ReLICv2
77.9%
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
DeepLab-CRF (ResNet-101)
77.69%
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
FastDenseNas-arch2
77.3%
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
DetCon
77.3%
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
FastDenseNas-arch1
77.1%
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
DeepLabv3 (ImageNet+300M)
76.5%
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
BYOL
75.7%
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
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