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Table 4 Comparison among the Dice coefficients of different models

From: Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

Model	Backbone	Animal	Building	Cloud	Disciple	Buddha
FCN	VGG16	0.812	0.784	0.841	0.825	0.763
SegNet	VGG16	0.854	0.817	0.876	0.86	0.792
DeepLabV3 +	Xception	0.873	0.834	0.915	0.871	0.817
MC-DM	MobileNetV2	0.867	0.832	0.872	0.881	0.81
SETR	ViT-L	0.88	0.853	0.876	0.864	0.825
DAFPN	Swin-T	0.907	0.851	0.874	0.906	0.822
MFAM (Ours)	CA_MobileViT	0.935	0.898	0.875	0.915	0.852

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