Image Classification On Resisc45
평가 지표
Top 1 Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Top 1 Accuracy |
|---|---|
| lwganet-a-lightweight-group-attention | 95.70 |
| vision-models-are-more-robust-and-fair-when | 88.56 |
| 모델 3 | 92.53 |
| decouplenet-a-lightweight-backbone-network | 95.87 |
| local-semantic-enhanced-convnet-for-aerial | 93.49 |
| a-multiple-instance-densely-connected-convnet | 87.99 |
| lwganet-a-lightweight-group-attention | 95.49 |
| vision-models-are-more-robust-and-fair-when | 92.48 |
| vision-models-are-more-robust-and-fair-when | 89.77 |
| sag-vit-a-scale-aware-high-fidelity-patching | - |
| vision-models-are-more-robust-and-fair-when | 93.35 |
| vision-models-are-more-robust-and-fair-when | 94.73 |
| in-domain-representation-learning-for-remote-1 | 96.83 |
| vision-models-are-more-robust-and-fair-when | 85.4 |
| vision-models-are-more-robust-and-fair-when | 95.61 |
| vision-models-are-more-robust-and-fair-when | 92.7 |
| vision-models-are-more-robust-and-fair-when | 93.97 |
| all-grains-one-scheme-agos-learning-multi | 94.91 |
| lwganet-a-lightweight-group-attention | 96.17 |