Dichotomous Image Segmentation On Dis Te4

評価指標

E-measure

HCE

MAE

S-Measure

max F-Measure

weighted F-measure

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

比較表

モデル名	E-measure	HCE	MAE	S-Measure	max F-Measure	weighted F-measure
hyperseg-patch-wise-hypernetwork-for-real	0.842	3331	0.091	0.802	0.782	0.693
u-2-net-going-deeper-with-nested-u-structure	0.847	3653	0.087	0.807	0.795	0.705
searching-for-mobilenetv3	0.848	3817	0.098	0.770	0.736	0.664
190807919	0.854	3864	0.092	0.792	0.772	0.687
revisiting-image-pyramid-structure-for-high	0.926	2243	0.046	0.891	0.892	0.840
icnet-for-real-time-semantic-segmentation-on	0.837	3690	0.099	0.776	0.749	0.663
basnet-boundary-aware-salient-object	0.848	3601	0.091	0.794	0.780	0.693
highly-accurate-dichotomous-image	0.87	2888	0.072	0.83	0.827	0.753
revisiting-image-pyramid-structure-for-high	0.926	2336	0.042	0.905	0.905	0.848
u-net-convolutional-networks-for-biomedical	0.821	3218	0.102	-	0.759	0.659
camouflaged-object-segmentation-with	0.838	3803	0.107	0.763	0.731	0.647
global-context-aware-progressive-aggregation	0.767	3678	0.127	0.723	0.670	0.559
concealed-object-detection	0.824	3683	0.113	0.744	0.699	0.616
rethinking-atrous-convolution-for-semantic	0.820	3709	0.111	0.744	0.715	0.621
pyramid-scene-parsing-network	0.815	3806	0.107	0.758	0.725	0.630
bisenet-bilateral-segmentation-network-for	0.788	3999	0.114	0.755	0.710	0.598
rethinking-bisenet-for-real-time-semantic	0.841	3819	0.102	0.762	0.731	0.652
f3net-fusion-feedback-and-focus-for-salient	0.825	3760	0.107	0.752	0.721	0.633
bilateral-reference-for-high-resolution	0.937	2746	0.040	0.898	0.900	0.861
suppress-and-balance-a-simple-gated-network	0.803	3654	0.109	0.743	0.729	0.625
multi-view-aggregation-network-for	0.944	2331	0.041	0.903	0.912	0.857
patch-depth-fusion-dichotomous-image	0.941	-	0.037	0.910	0.911	0.867