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SOTA
Semantic Segmentation
Semantic Segmentation On Camvid
Semantic Segmentation On Camvid
Metrics
Mean IoU
Results
Performance results of various models on this benchmark
Columns
Model Name
Mean IoU
Paper Title
SERNet-Former
84.62
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
SIW
83.7
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings
DSNet-Base
83.32
DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation
RTFormer-Base
82.5
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
PIDNet-Wider
82.0%
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
DeepLabV3Plus + SDCNetAug
81.7%
Improving Semantic Segmentation via Video Propagation and Label Relaxation
DDRNet23
80.6%
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
ETC-Mobile
76.3
Efficient Semantic Video Segmentation with Per-frame Inference
VideoGCRF
75.2
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
DenseDecoder
70.9
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
BiSeNet
68.7%
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
FC-DenseNet103
66.9%
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
EDANet
66.4
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Dilated Convolutions
65.3%
Multi-Scale Context Aggregation by Dilated Convolutions
DFANet A
64.7%
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
Template-Based NAS-arch0 (480x360 inputs)
63.9%
Template-Based Automatic Search of Compact Semantic Segmentation Architectures
LMDNet
63.5
Efficient Road Lane Marking Detection with Deep Learning
Template-Based NAS-arch1 (480x360 inputs)
63.2%
Template-Based Automatic Search of Compact Semantic Segmentation Architectures
DeepLab-MSc-CRF-LargeFOV
61.6%
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
ReSeg
58.8%
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
0 of 21 row(s) selected.
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