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Semantic Segmentation On Pascal Voc 2012 Val

Metrics

mIoU

Results

Performance results of various models on this benchmark

Paper Title
EfficientNet-L2+NAS-FPN (single scale test, with self-training)90.0%Rethinking Pre-training and Self-training
TADP87.11%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-JFT82.7%Rethinking Atrous Convolution for Semantic Image Segmentation
Auto-DeepLab-L82.04%Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
ResNet-GCN81.0%Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
HyperSeg-L80.61%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-arch078.0%Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
ReLICv277.9%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-arch277.3%Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
DetCon77.3%Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
FastDenseNas-arch177.1%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
BYOL75.7%Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
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