# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 3 Results
### Experimental Data
#### 3.3.1 Accuracy  
When the data augmentation is not performed, the accuracy comparison of each model under 10 epochs is illustrated in Figure 10a, and the results are listed in Table 4. From Figure 10b and Table 4, it can be seen that before data augmentation, the accuracy of each model is higher. Except for the slightly lower accuracy of Vision Transformer, the accuracy of the other models can reach more than 99%. Moreover, the accuracy of the model does not fluctuate much in 10 epochs, and the model is relatively stable. Vision Transformer has the lowest accuracy rate among the seven models, but it can also reach more than 94%, indicating that Vision Transformer has broad space for future applications in the field of computer vision. Take the maximum accuracy of each model in 10 epochs for comparison, and the columnar analysis chart is illustrated in Figure 10a. From the comparative analysis in Figure 10b, it can be seen that before data augmentation, AlexNet has the highest accuracy, reaching 99.800%. Except for Vision Transformer, LeNet5 has the lowest accuracy. This is due to the relatively simple structure of LeNet and the relatively deep model network. The accuracy of InceptionA and InceptionE improve on LeNet5 and are higher than that of LeNet5. This is due to the use of the Inception module to further deepen the network model. The accuracy of the ResNet18 network model can also reach 99.750%. Because its depth is the highest among the seven network models, its accuracy is also higher. However, due to the small dataset and fewer training images, ResNet18 failed to give full play to its functionality, and the accuracy rate was slightly lower than that of AlexNet. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **LeNet5** & **AlexNet** & **InceptionA** & **InceptionE** & **ResNet18** & **MobileNet** & **Vision Transformer** \\ \hline Parameters & 657.35 K & 0.47 GMac & 0.73 GMac & 59.59 GMac & 6.92 GMac & 0.58 GMac & 11.27 GMac \\ FLOPs & 61.33 K & 56.12 M & 29.75 M & 697.84 M & 11.18 M & 3.21 M & 85.80 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total number of parameters and floating point operations for each model. The quality of the original dataset is very high before data augmentation. There is not much noise in the dataset. In this case, the accuracy of nearly all models is very high. However, the original dataset is not generic; that is, only very good samples are collected in the dataset. Therefore, after data augmentation, many lower quality samples are created and used to train the models. In this case, the data augmentation operation on the dataset increases the training deviation of the original dataset, resulting in a decrease in accuracy. To further analyze the generalization ability and stability of each model, Figure 11 illustrates the comparison of accuracy rates under 10 epochs before and after data augmentation for the same network model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Epoch** & **LeNet5 (\%)** & **AlexNet (\%)** & **InceptionA (\%)** & **InceptionE (\%)** & **ResNet18 (\%)** & **MobileNet (\%)** & **Vision Transformer (\%)** \\ \hline
1 & 98.600 & 99.312 & 98.513 & 97.588 & 95.875 & 98.013 & 94.263 \\
2 & 98.812 & 99.375 & 98.875 & 99.000 & 99.562 & 99.025 & 94.588 \\
3 & 98.912 & 99.575 & 99.237 & 99.263 & 99.638 & 99.400 & 94.388 \\
4 & 99.000 & 99.688 & 99.263 & 99.275 & 99.200 & 99.638 & 94.513 \\
5 & 98.800 & 99.125 & 99.312 & 98.862 & 99.688 & 98.950 & 94.912 \\
6 & 98.737 & 99.650 & 99.287 & 99.075 & 99.700 & 99.525 & 94.588 \\
7 & 99.025 & 99.675 & 99.213 & 99.013 & 99.575 & 99.513 & 94.662 \\
8 & 99.050 & 99.150 & 99.100 & 99.150 & 99.750 & 99.588 & 92.463 \\
9 & 99.138 & 99.675 & 99.362 & 99.188 & 99.438 & 99.388 & 94.237 \\
10 & 99.037 & 99.800 & 99.138 & 99.100 & 99.388 & 99.537 & 94.263 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of crack identification before data augmentation. Figure 10: Comparison of accuracy before data augmentation.