Dichotomous Image Segmentation On Dis Te2

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
u-2-net-going-deeper-with-nested-u-structure	0.833	490	0.085	0.788	0.756	0.668
highly-accurate-dichotomous-image	0.858	340	0.07	0.823	0.799	0.728
basnet-boundary-aware-salient-object	0.836	480	0.084	0.786	0.755	0.668
searching-for-mobilenetv3	0.856	600	0.083	0.777	0.743	0.672
suppress-and-balance-a-simple-gated-network	0.804	501	0.102	0.737	0.702	0.598
bilateral-reference-for-high-resolution	0.935	265	0.035	0.904	0.898	0.863
pyramid-scene-parsing-network	0.828	586	0.092	0.763	0.724	0.636
rethinking-atrous-convolution-for-semantic	0.813	516	0.105	0.729	0.681	0.587
icnet-for-real-time-semantic-segmentation-on	0.826	512	0.095	0.759	0.716	0.627
global-context-aware-progressive-aggregation	0.786	574	0.109	0.735	0.673	0.570
revisiting-image-pyramid-structure-for-high	-	255	-	0.905	0.894	-
f3net-fusion-feedback-and-focus-for-salient	0.820	542	0.097	0.755	0.712	0.620
multi-view-aggregation-network-for	0.944	251	0.030	0.915	0.916	0.874
patch-depth-fusion-dichotomous-image	0.947	-	0.028	0.924	0.921	0.885
190807919	0.840	555	0.087	0.784	0.747	0.664
rethinking-bisenet-for-real-time-semantic	0.834	556	0.092	0.759	0.720	0.636
bisenet-bilateral-segmentation-network-for	0.781	621	0.111	0.740	0.680	0.564
concealed-object-detection	0.823	593	0.099	0.753	0.700	0.618
revisiting-image-pyramid-structure-for-high	0.925	316	0.038	0.893	0.881	0.834
hyperseg-patch-wise-hypernetwork-for-real	0.832	451	0.085	0.794	0.759	0.667
camouflaged-object-segmentation-with	0.829	567	0.096	0.761	0.720	0.633
u-net-convolutional-networks-for-biomedical	-	474	0.107	0.755	0.703	0.597