Semantic Segmentation on MF (test) benchmark leaderboard · SOTA2 Research

Semantic Segmentation on MF (test)

93.3Car AccuracyFEANet

Evaluation Results

Method	Links
FEANet2022.04		93.3	73.2	55.3	87.8	82.7	71.1	76.7	61.1	65.5	46.5	26.6	22.1	70.8	6.6	66.6	55.3	77.3	48.9
FuseSeg-1612022.04		93.1	70.6	54.5	87.9	81.4	71.7	78.5	64.6	68.4	44.8	29.1	22.7	63.7	6.4	55.8	46.9	66.4	47.9
RTFNet-1522022.04		93	63.1	53.2	87.4	79.3	70.3	76.8	62.7	60.7	45.3	38.5	29.8	0	0	45.5	29.1	74.7	55.7
HRNet2-4c2022.04		92.8	63.7	53.2	87.6	79.3	71	78.3	63.4	59.8	42.5	25.7	19.1	18.8	0	56.5	49.8	63.5	44.5
HRNet2-3c2022.04		92.2	60.9	51.3	86.6	73.1	59.8	74.9	61.3	47	33.2	23.8	28.7	7.3	0	54.6	47.2	61.5	46.2
DooDLeNet2022.04		91.7	67.9	57.3	86.7	81.3	72.2	76.6	62.5	58.9	46.7	36.2	28	35.2	5.1	56.9	50.7	74.8	65.8
RTFNet-502022.04		91.3	62.2	51.7	86.3	78.2	67.8	71.5	58.2	69.8	43.7	32.1	24.3	13.4	3.6	40.4	26	73.5	57.2
DFN-3c2022.04		90.7	57.3	47.5	81.4	67.7	52.8	71.5	57.5	49.2	34.9	35.1	23.8	4.1	1.4	44.2	31	54.6	47.5
BiSeNet-3c2022.04		90	54.9	48.2	84.5	65	54.3	75	61.4	32.1	25.7	32.3	26.2	3.2	0.9	49.6	43.3	48.1	40.5
DFN-4c2022.04		90	60.5	52	84.4	73.2	65	75.5	60.9	54	40.4	38.9	25.7	10.2	2.7	48.3	42.5	55.8	47.4
BiSeNet-4c2022.04		89.7	57.7	50	84.1	72	63.2	74.1	60.1	45.1	36.7	34.2	25.3	18.2	5	47.4	42.2	39.8	35.9
DACNN2022.04		85.2	55.1	46.1	77	61.7	53.4	76	56.5	40.2	30.9	9.9	29.3	22.8	6.4	32.9	30.1	36.5	32.3
FRRN-4c2022.04		81.9	48.5	44.2	74.7	66.2	60.8	62.8	50.3	41.2	35	12.5	11.5	0	0	37.2	34	35.2	34.6
FuseNet2022.04		81	52.4	45.6	75.6	75.2	66.3	64.5	51.9	51	37.8	28.7	15	0	0	31.1	21.4	51.9	45
FRRN-3c2022.04		80	47.1	41.8	71.2	53	46.1	65.1	53	34	27.1	21.6	19.1	0	0	34.7	32.5	36.2	30.5
MFNet2022.04		77.2	45.1	39.7	65.9	67	58.9	53.9	42.9	36.2	29.9	19.1	9.9	0.1	8.5	30.3	25.2	30	27.7