Semantic Segmentation on Wind Blade Segmentation (test) benchmark leaderboard · SOTA2 Research

Semantic Segmentation on Wind Blade Segmentation (test)

98.25AccuracyMARG+RegionMix

Evaluation Results

Method	Links
MARG+RegionMix2026.01		98.25	98.43	96.49	97.67	95.33	—	—
SAM22026.01		97.86	97.46	94.82	95.84	94.82	—	—
CSDA(ln)2026.01		97.4	95.72	97.38	96.17	93.72	94.25	93.18
BU-Net2026.01		97.39	99.42	93.35	95.73	93.8	94.7	92.9
BU-Net2026.01		97.39	99.42	93.35	95.73	93.8	—	—
Mask2Former-FreqFusion2026.01		97.35	98.02	96.12	96.67	94.65	—	—
CSDA(∆)2026.01		97.34	95.97	97.3	96.2	93.94	94.51	93.38
CSDA2026.01		96.98	96.78	96.65	96.13	93.86	94.21	93.51
Mask2Former2026.01		96.68	95.63	93.89	94.76	93.72	94.49	92.93
Mask2Former2026.01		96.68	95.63	93.89	94.76	93.72	—	—
EfficientFormer2026.01		96.42	95.47	93.63	94.55	93.51	94.02	92.99
EfficientFormer2026.01		96.42	95.47	93.63	94.55	93.81	—	—
DiffSeg2026.01		96.37	82.08	89.74	85.73	86.4	91.66	81.13
DiffSeg2026.01		96.37	89.74	85.73	86.4	72.14	—	—
U-NetFormer2026.01		96.2	97.31	93.51	94.42	91.75	92.53	90.96
U-NetFormer2026.01		96.2	97.31	93.51	94.42	91.75	—	—
MobileViT2026.01		96.14	95.44	93.33	94.38	93.47	94.06	92.88
MobileViT2026.01		96.14	95.44	93.33	94.38	93.47	—	—
BiRefNet2026.01		95.65	98.37	92.52	94.57	92.38	—	—
SAM2026.01		94.36	97.29	91.22	92.6	91.66	92.31	91.01
SAM2026.01		94.36	97.29	91.22	92.6	91.66	—	—
ResNeSt2026.01		94.23	96.84	91.47	92.77	89.63	90.4	88.86
ResNeSt2026.01		94.23	96.84	91.47	92.77	89.63	—	—
DeepLabv3+2026.01		94.14	96.36	87.38	89.03	87.47	90.31	84.62
DeepLabv3+2026.01		94.14	96.36	87.38	89.03	87.47	—	—
SW2026.01		93.48	93.57	91.71	91.37	87.44	88.64	86.23
SW2026.01		93.48	93.57	91.71	91.37	87.44	—	—
CLIPSeg2026.01		82.7	77.02	75.52	74.29	75.09	80.16	70.02
CLIPSeg2026.01		82.7	77.02	75.52	74.29	75.09	—	—
PDDA(ln)2026.01		0.9797	0.9466	0.9871	0.9628	0.9434	0.9528	0.934
PDDA(∆)2026.01		0.9762	0.9429	0.9779	0.9553	0.9329	0.9436	0.9221
BU-Net2026.01		0.9739	0.9942	0.9335	0.9573	0.938	0.947	0.929
Mask2Former2026.01		0.9668	0.9563	0.9389	0.9476	0.9372	0.9451	0.9293
EfficientFormer2026.01		0.9642	0.9547	0.9363	0.9455	0.9351	0.9402	0.9299
DiffSeg2026.01		0.9637	0.8208	0.8974	0.8573	0.864	0.9166	0.8113
MobileViT2026.01		0.9614	0.9544	0.9333	0.9438	0.9347	0.9406	0.9288
SAM2026.01		0.9436	0.9729	0.9122	0.926	0.9166	0.9231	0.9101
ResNeSt2026.01		0.9423	0.9684	0.9147	0.9277	0.8963	0.904	0.8886
DeepLabv3+2026.01		0.9414	0.9636	0.8738	0.8903	0.8747	0.9031	0.8462
SW2026.01		0.9348	0.9357	0.9171	0.9137	0.8744	0.8864	0.8623
U-Net2026.01		0.8624	0.9551	0.6893	0.7795	0.7594	0.8031	0.7157
CLIPSeg2026.01		0.827	0.7702	0.7552	0.7429	0.7509	0.8016	0.7002