Scene Classification on NWPU (10/90 split)
94.56AccuracyViT-B
Evaluation Results
| Method | Links | |
|---|---|---|
| ViT-BPretrain=MAE, Backbone=ViT-B, Method=—2022.08 | 94.56 | |
| ViTAE-BPretrain=MAE, Backbone=ViTAE-B, Method=—2022.08 | 94.43 | |
| VITAEv2-SPretrain=RSP, Backbone=VITAEv2-S, Method=—2022.08 | 94.41 | |
| RingMoPretrain=RingMo [32], Backbone=Swin-B, Method=—2022.08 | 94.25 | |
| ViTAE-B + VSAPretrain=MAE, Backbone=ViTAE-B + VSA, Method=—2022.08 | 93.98 | |
| ViTAE-B + RVSAPretrain=MAE, Backbone=ViTAE-B + RVSA, Method=—2022.08 | 93.93 | |
| ViTAE-B + RVSA♦Pretrain=MAE, Backbone=ViTAE-B + RVSA*, Method=—2022.08 | 93.92 | |
| VITAEv2-SPretrain=IMP, Backbone=VITAEv2-S, Method=—2022.08 | 93.9 | |
| ViT-B + RVSAPretrain=MAE, Backbone=ViT-B + RVSA, Method=—2022.08 | 93.79 | |
| ViT-B + VSAPretrain=MAE, Backbone=ViT-B + VSA, Method=—2022.08 | 93.74 | |
| ViT-B + RVSA♦Pretrain=MAE, Backbone=ViT-B + RVSA*, Method=—2022.08 | 93.74 | |
| RingMoPretrain=RingMo [32], Backbone=ViT-B, Method=—2022.08 | 93.46 | |
| GRMANetPretrain=IMP, Backbone=ResNet-50, Method=GRMANet [73]2022.08 | 93.19 | |
| MGML-FENetPretrain=IMP, Backbone=DenseNet-121, Method=MGML-FENet [77]2022.08 | 92.91 | |
| F2BRBMPretrain=IMP, Backbone=ResNet-50, Method=F2BRBM [5]2022.08 | 92.74 | |
| Swin-TPretrain=IMP, Backbone=Swin-T, Method=—2022.08 | 92.73 | |
| LSENetPretrain=IMP, Backbone=VGG-16, Method=LSENet [42]2022.08 | 92.23 | |
| EAMPretrain=IMP, Backbone=ResNet-101, Method=EAM [74]2022.08 | 91.91 | |
| ViT-BPretrain=IMP, Backbone=ViT-B, Method=—2022.08 | 90.96 | |
| MSANetPretrain=IMP, Backbone=ResNet-101, Method=MSANet [75]2022.08 | 90.38 | |
| RBFFPretrain=IMP, Backbone=MobileNet-V2 [78], Method=RBFF [79]2022.08 | 84.59 |