## 3 Experimental Results
### Model Verification Experiment
#### 3.2.3 Model Comparative Experiment  
SE-ResNet calibrates the feature response in the channel direction by stacking the squeezing and actuating blocks together. While combining the self-adaptive directional derivative threshold with the channel attention mechanism, SARSN can learn more useful fine-grained feature information in a specific threshold range. As shown in Figure 15, we selected ResNet34, 50, and 101, SE-ResNet34, 50, and 101 and SARSN34, 50, and 101, respectively. The training and testing conditions remain the same. Figure 14: The confusion matrix. Figure 15: Comparison of classification performance of ResNet, SE-ResNet, and SARSN (**a**) 34 series, (**b**) 50 series, (**c**) 101 series. From the comparison results in the figure, it can be found that under with the same network depth, the testing accuracy of the SE-ResNet network embedded with the SE block is better than that of the basic classification network ResNet, while the classification accuracy of SARSN proposed in this paper is the best of any model structure with any depth, among which the SARSN101 network reaches 97.83%. This shows that SARSN has the best effect in processing few-shot unbalanced data of strip steel. To verify the classification performance of different network models for strip steel defects, this paper uses GoogLeNet, DenseNet, InceptionV3, SqueezeNet, MobileNet, ResNet101, ResNext101, SE-ResNet101, and Teacher-SARSN101 Student-ResNet34 networks for comparative experiments. The test accuracy curve of each model is shown in Figure 16. Moreover, the classification accuracy, parameters and single image classification algorithm time consumption of each network model are shown in Table 6.  
It can be found that the test curve of the SARSN101 network rises gently, and its robustness is obviously better than that of other networks in the face of unbalanced datasets with few samples. Moreover, InceptionV3 is most obviously affected by the characteristics of the datasets, and its robustness is poor before 50 epochs. The testing accuracy of the SARSN101 network reaches 98.3%, which is much higher than that of the other models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Test Accuracy** & **Parameter Number** & **Time-Consumption/(ms)** \\ \hline GoogLeNet & 0.9407 & 6,306,214 & 58 \\ DenseNet & 0.9413 & 6,952,198 & 96 \\ InceptionV3 & 0.8011 & 22,125,542 & 248 \\ SqueezeNet & 0.9633 & 732,934 & 104 \\ MobileNetV2 & 0.8567 & 3,219,078 & 7.8 \\ ResNet101 & 0.8017 & 42,504,774 & 112 \\ ResNext101 & 0.9167 & 14,788,772 & 67 \\ SE-ResNet101 & 0.8451 & 47,527,764 & 187 \\ SARSN101 & 0.9783 & 46,127,174 & 85 \\ Teacher-SARSN101 & 0.9440 & 11,171,910 & 51 \\ Student-ResNet34 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the classification results of various models. Figure 16: Accuracy curve of the classification test for each model. However, the test accuracy of the Teacher-SARSN 101 Student-ResNet 34 network using the relational knowledge distillation method is 94.4%, which is better than other network models in terms of the comprehensive performance of three indices. To evaluate the method proposed in this paper more objectively, in addition to verifying that our proposed model has more advantages in accuracy and efficiency compared with other classification models, the following is also a comparison of different classification methods. After analysis, the texture information of six kinds of defects on the surface of strip steel is quite different, which is convenient for feature extraction. Four different feature extraction methods and classifier combinations were selected for comparison experiments with the methods proposed in this paper. In order to control the variance of the variables, all experiments selected identical datasets.