Dichotomous Image Segmentation On Dis Te3

E-measure

HCE

MAE

S-Measure

max F-Measure

weighted F-measure

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle

Modellname	E-measure	HCE	MAE	S-Measure	max F-Measure	weighted F-measure
190807919	0.869	1049	0.080	0.805	0.784	0.700
f3net-fusion-feedback-and-focus-for-salient	0.848	1059	0.092	0.773	0.743	0.656
pyramid-scene-parsing-network	0.843	1111	0.092	0.774	0.747	0.657
suppress-and-balance-a-simple-gated-network	0.815	972	0.103	0.747	0.726	0.620
camouflaged-object-segmentation-with	0.854	1082	0.092	0.777	0.751	0.664
bilateral-reference-for-high-resolution	0.952	573	0.030	0.918	0.923	0.891
bisenet-bilateral-segmentation-network-for	0.801	1146	0.109	0.757	0.710	0.595
revisiting-image-pyramid-structure-for-high	0.938	522	0.034	0.918	0.919	0.871
highly-accurate-dichotomous-image	0.883	687	0.064	0.836	0.830	0.758
icnet-for-real-time-semantic-segmentation-on	0.852	1001	0.091	0.780	0.752	0.664
concealed-object-detection	0.849	1096	0.096	0.766	0.730	0.641
multi-view-aggregation-network-for	0.954	525	0.031	0.920	0.929	0.890
u-2-net-going-deeper-with-nested-u-structure	0.858	965	0.079	0.809	0.798	0.707
searching-for-mobilenetv3	0.880	1136	0.078	0.764	0.772	0.702
patch-depth-fusion-dichotomous-image	0.957	-	0.027	0.928	0.936	0.900
rethinking-atrous-convolution-for-semantic	0.833	999	0.102	0.749	0.717	0.623
u-net-convolutional-networks-for-biomedical	-	883	0.098	-	0.748	0.644
revisiting-image-pyramid-structure-for-high	0.938	582	0.038	0.902	0.904	0.856
global-context-aware-progressive-aggregation	0.801	1058	0.109	0.748	0.699	0.590
hyperseg-patch-wise-hypernetwork-for-real	0.857	887	0.079	0.811	0.792	0.701
basnet-boundary-aware-salient-object	0.856	948	0.083	0.798	0.785	0.696
rethinking-bisenet-for-real-time-semantic	0.855	1081	0.090	0.771	0.745	0.662