Face Parsing on CelebAMask-HQ

0.995Nose AccuracyCelebA-HQ-Mask

Evaluation results

Method
CelebA-HQ-Mask2023.09	0.995	—	—	—	—	—	—	—	—	—	—	—	0.989	—	—	—	—	—	—	0.98	0.779	0.994	0.947
Dataset Diffusion2023.09	0.972	—	—	—	—	—	—	—	—	—	—	—	0.979	—	—	—	—	—	—	0.953	0.615	0.976	0.899
DatasetGAN2023.09	0.966	—	—	—	—	—	—	—	—	—	—	—	0.975	—	—	—	—	—	—	0.953	0.481	0.957	0.87
Te et al.2021.01	0.94	0.962	0.923	0.886	0.887	0.857	0.852	0.88	0.857	0.95	0.889	0.912	0.949	0.876	0.683	0.276	0.894	0.853	0.851	—	—	—	—
Te et al.2021.01	0.94	0.962	0.923	0.886	0.887	0.857	0.852	0.88	0.857	0.95	0.889	0.912	0.949	0.876	0.683	0.276	0.894	0.853	0.851	—	—	—	—
Te et al.2021.01	0.94	0.962	0.923	0.886	0.887	0.857	0.852	0.88	0.857	0.95	0.889	0.912	0.949	0.876	0.683	0.276	0.894	0.853	0.851	—	—	—	—
Te et al.2021.01	0.94	0.962	0.923	0.886	0.887	0.857	0.852	0.88	0.857	0.95	0.889	0.912	0.949	0.876	0.683	0.276	0.894	0.853	0.851	—	—	—	—
AGRNet2021.01	0.939	0.965	0.918	0.887	0.891	0.855	0.856	0.881	0.887	0.92	0.891	0.911	0.952	0.872	0.696	0.328	0.899	0.849	0.855	—	—	—	—
AGRNet2021.01	0.939	0.965	0.918	0.887	0.891	0.855	0.856	0.881	0.887	0.92	0.891	0.911	0.952	0.872	0.696	0.328	0.899	0.849	0.855	—	—	—	—
AGRNet2021.01	0.939	0.965	0.918	0.887	0.891	0.855	0.856	0.881	0.887	0.92	0.891	0.911	0.952	0.872	0.696	0.328	0.899	0.849	0.855	—	—	—	—
AGRNet2021.01	0.939	0.965	0.918	0.887	0.891	0.855	0.856	0.881	0.887	0.92	0.891	0.911	0.952	0.872	0.696	0.328	0.899	0.849	0.855	—	—	—	—
Luo et al.2021.01	0.937	0.96	0.906	0.862	0.865	0.832	0.831	0.865	0.841	0.938	0.886	0.903	0.939	0.859	0.678	0.301	0.888	0.835	0.84	—	—	—	—
Luo et al.2021.01	0.937	0.96	0.906	0.862	0.865	0.832	0.831	0.865	0.841	0.938	0.886	0.903	0.939	0.859	0.678	0.301	0.888	0.835	0.84	—	—	—	—
Luo et al.2021.01	0.937	0.96	0.906	0.862	0.865	0.832	0.831	0.865	0.841	0.938	0.886	0.903	0.939	0.859	0.678	0.301	0.888	0.835	0.84	—	—	—	—
Luo et al.2021.01	0.937	0.96	0.906	0.862	0.865	0.832	0.831	0.865	0.841	0.938	0.886	0.903	0.939	0.859	0.678	0.301	0.888	0.835	0.84	—	—	—	—
Wei et al.2021.01	0.919	0.964	0.895	0.871	0.85	0.808	0.825	0.841	0.833	0.906	0.879	0.91	0.911	0.839	0.654	0.178	0.881	0.806	0.821	—	—	—	—
Wei et al.2021.01	0.919	0.964	0.895	0.871	0.85	0.808	0.825	0.841	0.833	0.906	0.879	0.91	0.911	0.839	0.654	0.178	0.881	0.806	0.821	—	—	—	—
Wei et al.2021.01	0.919	0.964	0.895	0.871	0.85	0.808	0.825	0.841	0.833	0.906	0.879	0.91	0.911	0.839	0.654	0.178	0.881	0.806	0.821	—	—	—	—
Wei et al.2021.01	0.919	0.964	0.895	0.871	0.85	0.808	0.825	0.841	0.833	0.906	0.879	0.91	0.911	0.839	0.654	0.178	0.881	0.806	0.821	—	—	—	—
Zhao et al.2021.01	0.903	0.948	0.758	0.799	0.801	0.773	0.78	0.756	0.731	0.898	0.871	0.888	0.904	0.582	0.657	0.194	0.827	0.642	0.762	—	—	—	—
Zhao et al.2021.01	0.903	0.948	0.758	0.799	0.801	0.773	0.78	0.756	0.731	0.898	0.871	0.888	0.904	0.582	0.657	0.194	0.827	0.642	0.762	—	—	—	—
Zhao et al.2021.01	0.903	0.948	0.758	0.799	0.801	0.773	0.78	0.756	0.731	0.898	0.871	0.888	0.904	0.582	0.657	0.194	0.827	0.642	0.762	—	—	—	—
Zhao et al.2021.01	0.903	0.948	0.758	0.799	0.801	0.773	0.78	0.756	0.731	0.898	0.871	0.888	0.904	0.582	0.657	0.194	0.827	0.642	0.762	—	—	—	—
Lee et al.2021.01	0.856	0.955	0.929	0.843	0.852	0.814	0.812	0.849	0.831	0.634	0.889	0.901	0.866	0.913	0.632	0.261	0.928	0.683	0.803	—	—	—	—
Lee et al.2021.01	0.856	0.955	0.929	0.843	0.852	0.814	0.812	0.849	0.831	0.634	0.889	0.901	0.866	0.913	0.632	0.261	0.928	0.683	0.803	—	—	—	—
Lee et al.2021.01	0.856	0.955	0.929	0.843	0.852	0.814	0.812	0.849	0.831	0.634	0.889	0.901	0.866	0.913	0.632	0.261	0.928	0.683	0.803	—	—	—	—
Lee et al.2021.01	0.856	0.955	0.929	0.843	0.852	0.814	0.812	0.849	0.831	0.634	0.889	0.901	0.866	0.913	0.632	0.261	0.928	0.683	0.803	—	—	—	—
Dataset Diffusion2023.09	0.851	—	—	—	—	—	—	—	—	—	—	—	0.972	—	—	—	—	—	—	0.745	0.412	0.931	0.782