## 4 Experiment and Result Analysis
### Experimental Result Analysis
#### 4.4.3 Comparison of Different Classification Algorithms  
In order to verify the effectiveness of the model proposed in this paper, in this experiment, we compared our model with the VGG series and ResNet series models, and the test results are shown in Table 6. The experimental data in Table 6 show that the RepVGG-SimAM proposed in this paper is superior to other experimental models in terms of precision, recall, and F1 value in all categories. In addition, the actual Internet environment is more sensitive to the false alarm rate of bad pictures, so we also set up experiments to compare the different methods' false alarm rates (FARs) for bad pictures. The experimental data show that the model proposed in this paper performs best in terms of the FAR. The experimental results show that our method is superior to other deep neural network models in terms of feature extraction ability.  
In order to reduce the harm caused by the spread of bad pictures, the inference speed of the models is also an important evaluation index. In order to verify the inference speed of our method, we chose VGG11, VGG13, ResNet50, and ResNet101 for comparison with our method. The inference time of each model is shown in Figure 10. It can be seen from Figure 10 that the inference time of our proposed method is less than that of the other models, and with the increase in the number of images, the advantage of this model will be greater. This is mainly because although the VGG series models are single-channel models, the deep network structure will affect the reasoning speed of a model. Otherwise, the ResNet series models use more residual branch structures, and the neural network has high complexity and poor inference speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Precision (\%)**} & \multicolumn{4}{c}{**Recall (\%)**} & \multicolumn{4}{c}{**F1 (\%)**} & \multirow{2}{*}{**FAR**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{5-10}  & **Pom** & **Viololololololololololumn{1}{c}{**Norm**} & & & & & & & **Viololololololumn{1}{c}{**Norm**} & & **Pom** & **Viololololololumn{1}{c}{**Norm**} \\ \hline VGG11 & 90.1 & 91.9 & 83.2 & 88.9 & 94 & 80.9 & 89.5 & 92.9 & 82.1 & 9.8 & 89.4 \\ VGG13 & 87.6 & 92.3 & 82.6 & 88.9 & 93.9 & 78.2 & 88.3 & 93.1 & 80.3 & 10.2 & 89.8 \\ VGG16 & 87.5 & 91.7 & 80.1 & 89.9 & 92.8 & 77.5 & 88.7 & 92.3 & 78.7 & 9.7 & 88.7 \\ VGG19 & 89.8 & 90.1 & 81.3 & 86.8 & 94.5 & 79.5 & 88.4 & 92.3 & 80.4 & 9.6 & 88.5 \\ Resnet26 & 87.4 & 92.1 & 78.7 & 88.6 & 91.3 & 78.9 & 87.9 & 91.7 & 78.8 & 10.9 & 87.7 \\ Resnet34 & 89.3 & 92 & 77.8 & 88.7 & 90.5 & 79.1 & 88.9 & 91.3 & 78.4 & 12.1 & 86.9 \\ Resnet50 & 88 & 92.4 & 78.1 & 88.2 & 91.4 & 80 & 88.1 & 91.8 & 79 & 10.9 & 87.7 \\ Resnet101 & 87.8 & 92 & 78.8 & 86.9 & 91.1 & 79.8 & 87.4 & 91.6 & 79.3 & 10.6 & 87.9 \\ RepVGG-A2 & 90.1 & 93.8 & 82.9 & 89.4 & 94.3 & 83.9 & 90.1 & 94.1 & 83.4 & 6.5 & 90.8 \\ RepVGG-SimAM & 92.4 & 94.9 & 85.2 & 90.5 & 95.1 & 86.4 & 91.4 & 95 & 85.8 & 4.3 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results of each model.  
Figure 10: Model inference time.  
In summary, through experimental comparison, it has been found that the RepVGG-SimAM network model proposed in this paper is superior to the traditional method in terms of inference accuracy, the false alarm rate for bad images, and time consumed; it can better adapt to the current development of the Internet.