Semantic Segmentation On Bdd100K Val

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name	mIoU	Paper Title	Repository
Deeplabv3+	63.6	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation	-
DSNet-Base	64.6	DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation	-
DF1-Seg	42.5(82.3fps)	Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	-
SERNet-Former_v2	67.42	SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks	-
Bi-Align	53.4(42.1fps)	Fast and Accurate Scene Parsing via Bi-direction Alignment Networks	-
EMANet	61.4	Expectation-Maximization Attention Networks for Semantic Segmentation	-
BiSeNet-V1(ResNet-18)	53.8(45.1fps)	BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation	-
OCRNet	60.1	Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation	-
PSPNet	62.3	Pyramid Scene Parsing Network	-
SFNet-Lite(STDC2)	60.6(194.5FPS 4090)	SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow	-
ICNet	52.4(39.5fps)	ICNet for Real-Time Semantic Segmentation on High-Resolution Images	-
DF2-Seg	47.8(53.4fps)	Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	-
NiseNet	53.52	What's There in the Dark
SFNet(ResNet-18)	60.6(132.5FPS 4090)	Semantic Flow for Fast and Accurate Scene Parsing	-
VLTSeg	72.5	Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning	-
SFNet(DF1)	55.4(70.3fps)	Semantic Flow for Fast and Accurate Scene Parsing	-
STDC1	52.1(45.8FPS)	Rethinking BiSeNet For Real-time Semantic Segmentation	-
SFNet-Lite(ResNet-18)	60.6(161.3FPS 4090)	SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow	-
DSNet-head64	62.6(172.2FPS 4090)	DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation	-
STDC2	53.8(33.0FPS)	Rethinking BiSeNet For Real-time Semantic Segmentation	-

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