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SOTA
Semantische Segmentierung
Semantic Segmentation On Camvid
Semantic Segmentation On Camvid
Metriken
Mean IoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
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
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