Semantic Segmentation On Cityscapes

Metrics

Mean IoU (class)

Results

Performance results of various models on this benchmark

Model Name	Mean IoU (class)	Paper Title	Repository
ESANet-R34-NBt1D	80.09%	Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis	-
ESNet	70.7%	ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation
ResNeSt200 (Mapillary)	83.3%	ResNeSt: Split-Attention Networks
DUC-HDC (ResNet-101)	77.6%	Understanding Convolution for Semantic Segmentation
LightSeg-DarkNet19	70.75%	LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
AdapNet++	81.24%	Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
HRNetV2 + OCR (w/ ASP)	83.7%	Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
ShelfNet-34	79.0%	ShelfNet for Fast Semantic Segmentation
DeepLabv3 (ResNet-101, coarse)	81.3%	Rethinking Atrous Convolution for Semantic Image Segmentation
InternImage-H	86.1%	InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
SqueezeNAS (LAT Large)	72.5%	SqueezeNAS: Fast neural architecture search for faster semantic segmentation
OCNet	81.7%	OCNet: Object Context Network for Scene Parsing
MRFM(coarse)	83.0%	Multi Receptive Field Network for Semantic Segmentation	-
LiteSeg-MobileNet	67.81%	LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
HRNetV2 (train+val)	81.6%	Deep High-Resolution Representation Learning for Visual Recognition
ESPNetv2	66.2%	ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
LightSeg-MobileNet	67.81%	LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
DPN	66.8%	Semantic Image Segmentation via Deep Parsing Network
SPNet (ResNet-101)	82.0%	Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
HRNet (HRNetV2-W48)	81.6%	High-Resolution Representations for Labeling Pixels and Regions

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