Table 4 The classification results of the selected models on the proposed dataset.
Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---|---|---|---|---|
AlexNet | 77.67 | 70.55 | 69.16 | 69.20 |
ConvNeXt | 88.07 | 85.15 | 82.01 | 83.05 |
EfficientNet | 85.87 | 80.77 | 78.84 | 79.47 |
EfficientNetV2 | 85.36 | 82.54 | 78.68 | 79.98 |
MobileNetV2 | 84.98 | 81.59 | 78.21 | 79.23 |
MobileNetV3 | 84.87 | 80.86 | 77.96 | 78.99 |
ResNet50 | 80.19 | 73.08 | 68.92 | 70.38 |
ResNeXt50 | 85.68 | 81.12 | 78.33 | 79.25 |
SEResNet50 | 85.96 | 80.58 | 78.08 | 78.84 |
ShuffleNetV1 | 86.56 | 82.79 | 80.06 | 80.89 |
ShuffleNetV2 | 86.67 | 82.29 | 79.29 | 80.31 |
Swin Transformer | 85.96 | 82.30 | 79.33 | 80.30 |
Vision Transformer | 79.03 | 77.77 | 71.63 | 73.77 |
VGG | 82.07 | 75.45 | 72.81 | 73.55 |
