Classification on MSTAR 2-shot
52.98PrecisionEDM2(With Pretrained)
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| EDM2(With Pretrained)Train Iter.=15k2026.01 | 52.98 | 43.83 | 46.28 | 43.87 | |
| SiT(B/2)Train Iter.=15k2026.01 | 52.19 | 45.46 | 44.48 | 43.97 | |
| R3GANTrain Iter.=15k2026.01 | 51.66 | 40.65 | 41.01 | 40.51 | |
| EDM2Train Iter.=15k2026.01 | 50.41 | 46.52 | 43.27 | 42.91 | |
| Cr-GANTrain Iter.=15k2026.01 | 48.1 | 47.24 | 46.42 | 47.2 | |
| StyleGAN2 + DiffAugmentTrain Iter.=15k2026.01 | 47.31 | 44.11 | 42.98 | 42.74 | |
| DiT(B/2)Train Iter.=15k2026.01 | 46.41 | 34.97 | 37.44 | 37.44 | |
| StyleGAN2 + DA + RLCTrain Iter.=15k2026.01 | 46.04 | 31.94 | 33.27 | 34.69 | |
| StyleGAN2Train Iter.=15k2026.01 | 45.82 | 43.43 | 42.33 | 39.74 | |
| DDPMTrain Iter.=15k2026.01 | 44.6 | 40.68 | 39.79 | 38.69 | |
| DCGANTrain Iter.=15k2026.01 | 43.58 | 36.49 | 35.23 | 35.1 | |
| StyleGAN2 + ADATrain Iter.=15k2026.01 | 41.98 | 38.46 | 39.41 | 35.79 | |
| SwinTrain Iter.=-2026.01 | 39.68 | 28.69 | 25.76 | 20.77 | |
| Resnet18Train Iter.=-2026.01 | 37.82 | 33.22 | 34.7 | 33.35 | |
| Resnext101Train Iter.=-2026.01 | 37.34 | 30.41 | 25.19 | 21.22 | |
| SiT(S/2)Train Iter.=15k2026.01 | 36.85 | 33.41 | 32.48 | 30.71 | |
| RegnetTrain Iter.=-2026.01 | 34.68 | 36.75 | 31.96 | 29.78 | |
| Densenet121Train Iter.=-2026.01 | 34.34 | 28.35 | 27.01 | 22.63 | |
| ConvnextTrain Iter.=-2026.01 | 29.88 | 26.54 | 27.25 | 22.82 | |
| EfficientnetTrain Iter.=-2026.01 | 29.19 | 25.6 | 27.46 | 25.55 | |
| Resnet50Train Iter.=-2026.01 | 27.14 | 25.98 | 22.55 | 19 | |
| Swin v2Train Iter.=-2026.01 | 24.49 | 28.97 | 24.7 | 21.23 | |
| resnext50Train Iter.=-2026.01 | 22.77 | 29.32 | 27.3 | 21.32 | |
| ViT-BaseTrain Iter.=-2026.01 | 22.09 | 25.01 | 21.33 | 18.24 | |
| ViT-Base(finetune-head)Train Iter.=-2026.01 | 15.89 | 16.23 | 19.11 | 13.05 |