Semantic Segmentation On Ade20K

المقاييس

GFLOPs

Params (M)

Validation mIoU

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	GFLOPs	Params (M)	Validation mIoU	Paper Title	Repository
InternImage-L	2526	256	54.1	InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
TEC (Vit-B, Upernet)	-	-	51.0	Towards Sustainable Self-supervised Learning
EfficientViT-B3 (r512)	-	-	49	EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
VAN-Large	-	49	48.1	Visual Attention Network
MaskFormer(Swin-B)	-	-	53.8	Per-Pixel Classification is Not All You Need for Semantic Segmentation
CFNet(ResNet-101)	-	-	44.89	Co-Occurrent Features in Semantic Segmentation
ConvNeXt-S	-	82	49.6	A ConvNet for the 2020s
SegNet	-	-	21.64	SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
M3I Pre-training (InternImage-H)	-	1310	62.9	Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
HRViT-b2 (SegFormer, SS)	-	20.8	48.76	Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
SwinV2-G-HTC++ Liu et al. ([2021a])	-	-	53.7	Swin Transformer V2: Scaling Up Capacity and Resolution
SETR-MLA (160k, MS)	-	-	50.28	Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
ELSA-Swin-S	-	-	50.3	ELSA: Enhanced Local Self-Attention for Vision Transformer
Sequential Ensemble (DeepLabv3+)	-	-	46.8	Sequential Ensembling for Semantic Segmentation	-
FastViT-SA36	-	-	-	FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
ACNet (ResNet-101)	-	-	45.90	Adaptive Context Network for Scene Parsing	-
SGR (ResNet-101)	-	-	44.32	Symbolic Graph Reasoning Meets Convolutions
ConvMLP-S	-	-	35.8	ConvMLP: Hierarchical Convolutional MLPs for Vision
SeMask (SeMask Swin-L MSFaPN-Mask2Former)	-	-	58.2	SeMask: Semantically Masked Transformers for Semantic Segmentation
ConvMLP-M	-	-	38.6	ConvMLP: Hierarchical Convolutional MLPs for Vision

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