Image Classification On Gashissdb
평가 지표
Accuracy
F1-Score
Precision
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy | F1-Score | Precision |
|---|---|---|---|
| efficientnet-rethinking-model-scaling-for | 98.11 | 99.01 | 99.94 |
| coatnet-marrying-convolution-and-attention | 98.74 | 99.38 | 99.97 |
| deep-residual-learning-for-image-recognition | 98.47 | 99.19 | 99.94 |
| deep-residual-learning-for-image-recognition | 98.56 | 99.24 | 99.94 |
| aggregated-residual-transformations-for-deep | 98.59 | 99.25 | 99.94 |
| densely-connected-convolutional-networks | 96.90 | 98.38 | 99.91 |
| regnet-self-regulated-network-for-image | 97.48 | 98.70 | 99.97 |
| res2net-a-new-multi-scale-backbone | 98.68 | 99.29 | 99.91 |