## 3 Results and Analysis
### Performance of Models  
Eight ResNet models with different depths and five other classical models are trained, including ResNet-4, ResNet-6, ResNet-8, ResNet-10, ResNet-14, ResNet-18, ResNet-34, ResNet-50, DenseNet-121, EfficientNet B0, SqueezeNet1_0, MobileNet V2, and VGG-19. Table 3 summarizes the size of models, the average training time per epoch, the total parameters of the models, and the floating point of operations (FLOPs) of the models. The model's training time is affected by various factors such as the architecture of the model and the FLOPs of the model. Generally, for the same architecture, the more FLOPs of the model, the longer the training time. The maximum FLOPs and training time appeared on VGG-19 while the minimum FLOPs and training time appeared on ResNet-4. For residual networks, a greater number of FLOPs means more training time. The parameters of a model can reflect the complexity of the model. Generally, a higher number of parameters leads to a larger model size and increased complexity. Among the 13 network models, ResNet-4, ResNet-6, and SqueezeNet have fewer parameters, with none exceeding 1 M. The parameters of ResNet-8, MobileNet, EfficientNet, ResNet-10, ResNet-14, and DenseNet ranged from 1.227 M to 6.951 M. The parameters of ResNet-18, ResNet-34, ResNet-50, and VGG-19 were relatively high, with VGG-19 having the largest parameter of 139.581 M.  
The learning curves of the models are shown in Figure 5, where red lines indicate the accuracy and loss calculated on the training set, and blue lines represent the accuracy and loss calculated on the validation set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Model’s Size (MB)** & **Average Epoch Time (s)** & \
\begin{tabular}{c} **Model’s Total** \\ **Parameters (M)** \\ \end{tabular} &
\begin{tabular}{c} **Model’s Total** \\ **FLOPs (M)** \\ \end{tabular} \\ \hline ResNet-4 & 0.307 & 53.8 & 0.077 & 275.57 \\ ResNet-6 & 1.19 & 54.6 & 0.307 & 456.50 \\ SqueezeNet & 2.80 & 63.5 & 0.728 & 620.88 \\ ResNet-8 & 4.71 & 54.9 & 1.227 & 636.88 \\ MobileNet & 8.77 & 63.9 & 2.228 & 318.99 \\ EfficientNet & 15.6 & 70.7 & 4.011 & 406.64 \\ ResNet-10 & 18.7 & 55.4 & 4.901 & 816.99 \\ ResNet-14 & 20.1 & 56.3 & 5.270 & 1281.82 \\ DenseNet & 27.1 & 116.3 & 6.951 & 2817.31 \\ ResNet-18 & 42.7 & 57.5 & 11.172 & 1744.85 \\ ResNet-34 & 81.3 & 68.7 & 21.280 & 3599.55 \\ ResNet-50 & 90.0 & 96.2 & 23.508 & 4053.02 \\ VGG-19 & 532.48 & 185.3 & 139.581 & 19,570.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical table of model size, average training time per epoch, total parameters of models, and total FLOPs of models. Figure 5: The learning curves of ResNet-4, ResNet-6, SqueezeNet1_0, ResNet-8, MobileNet V2, EfficientNet B0, ResNet-10, ResNet-14, DenseNet-121, ResNet-18, ResNet-34, ResNet-50, and VGG-19. In order to accurately analyze the differences between models, the training and validation loss curves are analyzed in detail. Generally, the validation loss increases while the training loss decreases, indicating overfitting. The learning curves in Figure 5 show that ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 exhibit no overfitting, and with the complexity of the model increasing, the training loss shows improvement, but overfitting was observed in DenseNet-121, EfficientNet B0, MobileNet V2, VGG-19, and ResNet models from ResNet-10 to ResNet-50, and for most models with the severity of overfitting increasing as the complexity of the model increases. In fact, by increasing the complexity of the model, its feature extraction capability can be enhanced, leading to improved training loss. However, the excessively complex model may lead to an increase in validation loss and an increased risk of overfitting. In a word, the balance between training and validation loss is crucial. Notably, MobileNet V2 and EfficientNet B0 are more serious than ResNet-10 and ResNet-14 in overfitting, although MobileNet V2 and EfficientNet B0 have fewer parameters. Perhaps this is the advantage of the residual structure of ResNet.