## 3 Results and Analysis
### Performance of Models
To assess the model's performance on the test set, the weight with the lowest validation loss is chosen for making predictions on the test set. Each of all the models underwent three rounds of training and testing. Metrics are calculated from the average of three test results to mitigate the impact of sample randomness. Table 4 summarizes the F1 score (F1) for each model and each class as well as the macro-F1 score (Macro-F1) and accuracy (Acc) for each model. The highest scores are indicated in red while the lowest scores are indicated in blue. The models without overfitting are indicated in red. For the interlayer loosening, the F1 score ranges from 0.9239 to 0.9366, with SqueezeNet1_0 having the lowest and ResNet-8 achieving the highest. For the interlayer debonding, the F1 scores range from 0.9812 to 0.9853, with ResNet-34 being the top-performing model, while SqueezeNet1_0 scored the lowest. For the interlayer water seepage, the F1 score ranges from 0.9798 to 0.9846, with MobileNet V2 having the lowest score, and ResNet-6 and ResNet-8 having the highest. Overall, ResNet-8 performs best while SqueezeNet1_0 performs worst for the interlayer loosening. ResNet-34 performs best while SqueezeNet1_0 performs worst for the interlayer debonding. ResNet-6 and ResNet-8 demonstrate superior performance while MobileNet V2 performs the worst for the interlayer water seepage. In terms of macro-F1 score, SqueezeNet1_0 exhibited the lowest macro-F1 score; ResNet-8 achieves the highest macro-F1 score. For accuracy, ResNet-6, ResNet-8, and ResNet-34 achieve the highest scores, each reaching 0.9772, while SqueezeNet1_0 performs worst. In summary, ResNet-8 emerges as the top-performing model, followed by ResNet-6 and ResNet-34, while SqueezeNet1_0 performs worst. Overall, the macro-F1 score and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & **Loose\_1** & **Poor\_1** & **Water\_1** & & \\ \cline{2-5}  & **F1** & **F1** & **F1** & **Macro-F1** & **Acc** \\ \hline
**ResNet-4** & 0.9243 & 0.9827 & 0.9796 & 0.9622 & 0.9725 \\
**ResNet-6** & 0.9362 & 0.9848 & 0.9846 & 0.9685 & 0.9772 \\
**SqueezeNet1\_0** & 0.9239 & 0.9812 & 0.9813 & 0.9621 & 0.9724 \\
**ResNet-8** & 0.9366 & 0.9848 & 0.9846 & 0.9687 & 0.9772 \\
**MobileNetV2** & 0.9323 & 0.9838 & 0.9798 & 0.9653 & 0.9741 \\
**EfficientNetB0** & 0.9280 & 0.9828 & 0.9825 & 0.9644 & 0.9742 \\
**ResNet-10** & 0.9341 & 0.9846 & 0.9838 & 0.9675 & 0.9765 \\
**ResNet-14** & 0.9313 & 0.9843 & 0.9827 & 0.9661 & 0.9755 \\
**DenseNet-121** & 0.9265 & 0.9830 & 0.9837 & 0.9644 & 0.9746 \\
**ResNet-18** & 0.9314 & 0.9837 & 0.9825 & 0.9659 & 0.9752 \\
**ResNet-34** & 0.9363 & 0.9853 & 0.9839 & 0.9685 & 0.9772 \\
**ResNet-50** & 0.9298 & 0.9838 & 0.9826 & 0.9654 & 0.9749 \\
**VGG-19** & 0.9291 & 0.9823 & 0.9827 & 0.9647 & 0.9741 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics for the 13 models. accuracy of these models have reached a high level but the differences in the macro-F1 scores and accuracy of these models are small. Generally, overfitting will lead to the weak generalization performance of a model and the predictive risk on new data. The accuracy and macro-F1 scores of the 13 models were all at a high level with a small gap. MobileNet V2, EfficientNet B0, ResNet-10, ResNet-14, DenseNet-121, ResNet-18, ResNet-34, ResNet-50, and VGG-19 showed overfitting, and these models may have learned noise in the training data rather than the underlying data distribution which may be a risk for the predictions on new data. To compare each model's performance more intuitively, Figure 6 summarizes the confusion matrix of each model. The diagonal elements of the confusion matrix indicate the number of correct predictions made by the model for each class, while the remaining elements represent the number of incorrect predictions. Figure 6: Confusion matrixes of models. For the category of interlayer loosening, the number of correct predictions is between 1361 and 1426.