## 4 Results
### EfficientViT vs. CNN  
In this experiment, we compared the performance of EfficientViT with conventional CNN-based models (VGG-16, ResNeXt-50, DenseNet-121) under the one-stage classification framework. Evaluation metrics included accuracy, F1 score, precision, and recall. The training convergence behavior of the one-stage models is visualized in Appendix C. Figure A7, confirming consistent optimization across training, validation, and test sets. The results are summarized in Table 4.  
As shown in Table 4, EfficientViT achieved the highest performance across all evaluation metrics, with an accuracy of 98.46% and an F1 score of 0.9867. It also recorded the best precision (0.9874) and recall (0.9859), outperforming all CNN-based baselines. While ResNeXt-50 achieved comparable performance (accuracy 98.30%; F1 score 0.9847), DenseNet-121 and VGG-16 showed slightly lower performance. Overall, EfficientViT demonstrated superior accuracy and robustness compared to traditional convolutional models.  
Figure 5 illustrates the trade-off between inference speed and accuracy for all one-stage models. As shown in the scatter plot, EfficientViT achieves the highest accuracy among  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **F1 Score** & **Precision** & **Recall** \\ \hline ResNeXt-50 & 98.30 & 0.9847 & 0.9838 & 0.9855 \\ DenseNet-121 & 98.12 & 0.9828 & 0.9834 & 0.9822 \\ VGG-16 & 97.99 & 0.9825 & 0.9834 & 0.9816 \\ EfficientViT & **98.46** & **0.9867** & **0.9874** & **0.9859** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of EfficientViT and CNN models.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **F1 Score** & **Inference Speed (ms)** & **Param Size (MB)** \\ \hline U-Net + ResNeXt-50 & 98.40 & 0.9857 & 19.31 & 185.48 \\ Attention-U-Net + ResNeXt-50 & 98.31 & 0.9850 & 24.75 & 228.94 \\ SegNet + ResNeXt-50 & 98.30 & 0.9850 & 16.46 & 209.73 \\ DeepLab-V3+ + ResNeXt-50 & 98.40 & 0.9861 & 8.90 & 259.25 \\ EfficientPS + ResNeXt-50 & 98.34 & 0.9848 & 15.56 & 160.23 \\ MobileNetV2 + ResNeXt-50 & 98.42 & 0.9860 & 5.63 & 100.05 \\ ResNeXt-50 & 98.30 & 0.9847 & **3.26** & 91.96 \\ \hline U-Net + DenseNet-121 & 95.07 & 0.9577 & 21.85 & 122.39 \\ Attention-U-Net + DenseNet-121 & 95.14 & 0.9586 & 27.29 & 155.85 \\ SegNet + DenseNet-121 & 95.29 & 0.9600 & 19.00 & 146.64 \\ DeepLab-V3+ + DenseNet-121 & 95.12 & 0.9581 & 11.44 & 186.16 \\ EfficientPS + DenseNet-121 & 95.00 & 0.9569 & 18.10 & 97.14 \\ MobileNetV2 + DenseNet-121 & 95.27 & 0.9594 & 8.17 & 37.77 \\ DenseNet-121 & 98.12 & 0.9828 & 5.86 & **28.87** \\ \hline U-Net + VGG-16 & 96.99 & 0.9733 & 22.94 & 567.64 \\ Attention-U-Net + VGG-16 & 96.92 & 0.9723 & 28.38 & 601.10 \\ SegNet + VGG-16 & 96.95 & 0.9726 & 20.09 & 591.89 \\ DeepLab-V3+ + VGG-16 & 96.93 & 0.9724 & 12.53 & 631.41 \\ EfficientPS + VGG-16 & 96.81 & 0.9713 & 19.19 & 542.39 \\ MobileNetV2 + VGG-16 & 96.98 & 0.9729 & 9.26 & 483.02 \\ VGG-16 & 97.99 & 0.9825 & 6.90 & 474.12 \\ \hline Efficient-ViT & **98.46** & **0.9867** & 3.92 & 36.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between two-stage and one-stage models in terms of accuracy, F1 score, inference speed, and parameter size.  
the evaluated models while maintaining low inference latency, demonstrating a favorable balance between performance and efficiency.