## 3 Experimental Results
### Model Verification Experiment
#### 3.2.2 Student Model  
To meet the real-time requirements of industrial field classification tasks, this paper chooses the lightweight network ResNet34 for the deployment of the student model of relational knowledge distillation. The training accuracy rates achieved when using SARSN34, 50, and 101 as teacher models to train the student model ResNet34 and only training ResNet34 are shown in Table 4, which shows that the student model has learned the relevant model generalization ability from the teacher model. With the deepening of the teacher model network, the training accuracy of the student model is also improved. According to the analysis of Table 4, the migration effect of the model with SARSN34 as the teacher is 90.56%, which is higher than 89.18% for the ResNet34 network in the same period, and the model efficiency is greatly improved. This shows that the student model has learned the relevant model generalization ability from the teacher model. With the deepening of the teacher model network, the training accuracy of the student model is also improved. To verify the classification accuracy of the student model, aiming at six kinds of defects of strip steel, a total of 600 images were selected for testing, and the number of test rounds was 200 epochs. Taking the average accuracy of the current epochs as the test accuracy results, the average test accuracy values of SARSN34, SARSN50, and SARSN101 as the teacher model and ResNet34 as the student model were calculated to be 87.16%, 90.87%, and 94.40%, respectively. However, the average test accuracy rate of training the ResNet34 network alone is only 88.62%. The classification test accuracy results for six kinds of strip \begin{table}
\begin{tabular}{c c c} \hline \hline
**Teacher Mode** & **Student Model** & **Training Accuracy** \\ \hline SARSN34 & ResNet34 & 90.56\% \\ SARSN50 & ResNet34 & 92.31\% \\ SARSN101 & ResNet34 & 95.23\% \\  & ResNet34 & 89.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Student model training accuracy. Figure 12: Teacher model training loss and accuracy curve. (**a**) Teacher model training accuracy curve. (**b**) Teacher model training loss curve. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Teacher Mode** & **Training Accuracy** & **Testing Accuracy** \\ \hline SARSN34 & 97.80\% & 96.85\% \\ SARSN50 & 98.27\% & 97.14\% \\ SARSN101 & 98.33\% & 97.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Teacher Model Training and Testing Accuracy. steel defects are shown in Figure 13a,b. Figure 13a shows the test results of training the ResNet34 network independently without using relational knowledge distillation, and Figure 13b shows the test results of using SARSN50 as a teacher model and ResNet34 as a student model. According to the analysis of Figure 13, the classification accuracy of six kinds of defects after knowledge distillation is generally improved, while the accuracy of six kinds of defects after RKD is improved by \(-1.46\%\), \(2.25\%\), and \(5.78\%\) based on SARSN34, \(50\), and \(101\), respectively. This shows that the deeper the teacher model is, the more learnable parameters can be provided. To further evaluate the testing effect of the student model obtained by distillation of relational knowledge, experiments were conducted on the 600 testing sets of strip steel defects. Each class contains 100 images, and the confusion matrix is shown in Figure 14, in which the abscissa represents the predicted value of each class of strip steel defect data, and the ordinate represents the true value. The classification accuracy \(P\), the recall rate \(R\), \(F_{1}\in[0,1]\), and formulas of P, R and F1 are shown in Formula (21):  
\[P=\frac{N_{TP}}{N_{TP}+N_{FP}},R=\frac{N_{TP}}{N_{TP}+N_{FN}},F_{1}=\frac{2 \times P\times R}{P+R} \tag{21}\]  
where NTP is the number of positive samples with correct prediction; NFP is the number of positive samples with prediction errors; NTN is the number of negative samples with correct prediction; and NFN is the number of negative samples with prediction errors.