Semantic Segmentation on Cityscapes (val) (Specific Per-Class IoU Set) benchmark leaderboard · SOTA2 Research

Semantic Segmentation on Cityscapes (val) (Specific Per-Class IoU Set)

87.8Road IoUProDA

Evaluation Results

Method	Links
ProDA2021.01		87.8	45.7	84.6	37.1	0.6	44	54.6	37	88.1	84.4	74.2	24.3	88.2	51.1	40.5	45.6	55.5	20.6	62	21.7
Seg-Uncertainty2021.01		87.6	41.9	83.1	14.7	1.7	36.2	31.3	19.9	81.6	80.6	63	21.8	86.2	40.7	23.6	53.1	47.9	13	54.9	14.6
APODA2021.01		86.4	41.3	79.3	—	—	—	22.6	17.3	80.3	81.6	56.9	21	84.1	49.1	24.6	45.7	53.1	12.8	—	—
BDL2021.01		86	46.7	80.3	—	—	—	14.1	11.6	79.2	81.3	54.1	27.9	73.7	42.2	25.7	45.3	51.4	11.1	—	—
ADVENT2021.01		85.6	42.2	79.7	8.7	0.4	25.9	5.4	8.1	80.4	84.1	57.9	23.8	73.3	36.4	14.2	33	41.2	6.3	48	7.7
CAG_UDA2021.01		84.7	40.8	81.7	7.8	0	35.1	13.3	22.7	84.5	77.6	64.2	27.8	80.9	19.7	22.7	48.3	44.5	9.6	51.5	11.2
FADA2021.01		84.5	40.1	83.1	4.8	0	34.3	20.1	27.2	84.8	84	53.5	22.6	85.4	43.7	26.8	27.8	45.2	10.3	52.5	12.2
PatchAlign2021.01		82.4	38	78.6	8.7	0.6	26	3.9	11.1	75.5	84.6	53.5	21.6	71.4	32.6	19.3	31.7	40	5.1	46.5	6.2
CLAN2021.01		81.3	37	80.1	—	—	—	16.1	13.7	78.2	81.5	53.4	21.2	73	32.9	22.6	30.7	47.8	7.5	—	—
AdaptSeg2021.01		79.2	37.2	78.8	—	—	—	9.9	10.5	78.2	80.5	53.5	19.6	67	29.5	21.6	31.3	45.9	5.6	—	—
CBST2021.01		68	29.9	76.3	10.8	1.4	33.9	22.8	29.5	77.6	78.3	60.6	28.3	81.6	23.5	18.8	39.8	42.6	7.7	48.9	8.6
MRKLD2021.01		67.7	32.2	73.9	10.7	1.6	37.4	22.2	31.2	80.8	80.5	60.8	29.1	82.8	25	19.4	45.3	43.8	8.9	50.1	9.8
Source2021.01		64.3	21.3	73.1	2.4	1.1	31.4	7	27.7	63.1	67.6	42.2	19.9	73.1	15.3	10.5	38.9	34.9	0	40.3	0