Face Parsing on Helen (test)

0.909Eyes AccuracyDML-CSR

Evaluation results

Method
DML-CSR2022.03	0.909	0.885	0.955	0.912	0.876	0.912	0.959	—	0.966	—	—	0.938
Te et al.2021.01	0.902	0.849	0.961	0.898	0.836	0.91	0.955	—	0.946	—	0.932	—
Te et al. [36]2022.03	0.902	0.849	0.961	0.898	0.836	0.91	0.955	—	0.946	—	—	0.932
AGRNet2021.01	0.901	0.85	0.959	0.9	0.832	0.909	0.962	—	0.951	—	0.932	—
Liu et al.2021.01	0.898	0.835	0.958	0.891	0.837	0.914	0.961	—	0.949	—	0.931	—
Liu et al. [21]2022.03	0.898	0.835	0.958	0.891	0.837	0.914	0.961	—	0.949	—	—	0.931
Lin et al.2021.02	0.896	0.831	0.956	0.867	0.796	0.898	0.95	0.924	0.945	83.5	88.6	—
Lin et al.2021.01	0.896	0.831	0.956	0.867	0.796	0.898	0.95	—	0.945	—	0.927	—
Lin et al. [17]2022.03	0.896	0.831	0.956	0.867	0.796	0.898	0.95	—	0.945	—	—	0.924
Yin et al.2021.01	0.895	0.848	0.963	0.856	0.824	0.866	0.928	—	—	—	0.91	—
RTNet2021.02	0.893	0.849	0.949	0.899	0.941	0.909	0.956	0.927	0.962	90.6	91.8	—
Wei et al.2021.02	0.89	0.826	0.952	0.867	0.8	0.864	0.936	0.915	0.915	—	—	—
Wei et al.2021.01	0.89	0.826	0.952	0.867	0.8	0.864	0.936	—	0.956	—	0.917	—
Wei et al. [46]2022.03	0.89	0.826	0.952	0.867	0.8	0.864	0.936	—	0.956	—	—	0.916
Zhou et al.2021.02	0.874	0.813	0.95	0.836	0.754	0.809	0.926	0.873	—	—	—	—
Liu et al.2021.02	0.868	0.77	0.93	0.792	0.743	0.817	0.891	0.886	0.921	—	—	—
Liu et al.2021.01	0.868	0.77	0.93	0.792	0.743	0.817	0.891	—	0.921	—	0.886	—
Liu et al. [19]2022.03	0.868	0.77	0.93	0.792	0.743	0.817	0.891	—	0.921	—	—	0.886
Wei et al.2021.02	0.847	0.786	0.937	—	—	—	0.915	0.902	0.915	—	—	—
Guo et al. [7]2022.03	0.804	0.871	0.941	0.837	0.758	0.831	0.924	—	0.938	—	—	0.905
Smith et al.2021.02	0.785	0.722	0.922	0.713	0.651	0.7	0.857	0.804	0.882	—	—	—
Liu et al.2021.02	0.768	0.713	0.909	0.808	0.623	0.694	0.841	0.847	0.91	—	—	—