# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 3 Results
### Experimental Data
#### 3.3.2 Efficiency  
The calculation time of 10 epochs for each model is illustrated in Table 6, and the comparison chart is illustrated in Figure 13. Since the data are enhanced, the training dataset of each model is different in each epoch round; in this case, the operation time after data augmentation is not comparable. We only compare and analyze the calculation time for each epoch of each model before data augmentation and make a preliminary judgment on the calculation efficiency of each model.  
It can be seen in Figure 13 that the calculation times for each epoch of each model are not very different, and the calculation time for 10 epochs is evenly distributed. Among the models, InceptionE has the longest calculation time, LeNet5 has the shortest calculation time, which is due to the simplicity of the LeNet5 model, the minimal number of parameters and floating point operations, and its shallow depth. The Inception module used in the InceptionE model is more complex, which increases the depth and complexity of the model. The number of parameters and floating point operations of InceptionE is the highest among all models; thus, the computation time is long and the computation efficiency is low.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Epoch** & **LeNet5 (\%)** & **AlexNet (\%)** & **InceptionA (\%)** & **InceptionE (\%)** & **ResNet18 (\%)** & **MobileNet (\%)** & **Vision Transformer (\%)** \\ \hline
1 & 1.34 & 3.40 & 4.76 & 53.39 & 6.62 & 4.31 & 15.02 \\
2 & 1.33 & 3.32 & 4.30 & 53.24 & 6.73 & 4.25 & 15.02 \\
3 & 1.33 & 3.32 & 4.30 & 53.24 & 6.70 & 4.21 & 15.05 \\
4 & 1.34 & 3.31 & 4.28 & 53.30 & 6.69 & 4.37 & 15.00 \\
5 & 1.33 & 3.33 & 4.29 & 53.25 & 6.70 & 4.51 & 15.00 \\
6 & 1.33 & 3.33 & 4.26 & 53.25 & 6.69 & 4.54 & 15.01 \\
7 & 1.33 & 3.35 & 4.31 & 53.26 & 6.71 & 4.54 & 14.99 \\
8 & 1.34 & 3.33 & 4.29 & 53.29 & 6.78 & 4.54 & 15.00 \\
9 & 1.32 & 3.36 & 4.25 & 53.26 & 6.69 & 4.53 & 14.98 \\
10 & 1.32 & 3.32 & 4.31 & 53.27 & 6.72 & 4.52 & 15.00 \\ \hline \end{tabular}
\end{table}
Table 6: Operation timetable for each epoch of each model.  
The computational efficiency of the three models, i.e., AlexNet, InceptionA, and MobileNet, are similar, approximately 3-4 min. Among the three models, AlexNet is the fastest, MobileNet is the second fastest, and InceptionA is the slowest. The AlexNet model is computationally intensive, but the number of parameters is small, so it can achieve a faster operation speed. The MobileNet model has a large number of parameters but a small number of floating point operations, and its operating speed is greatly improved using the depth-separable convolution module. Thus, it can maintain a high operation efficiency even when the network model is deeper. InceptionA has the largest number of parameters and larger floating point operations, so its operation speed is the slowest.  
ResNet18 has a relatively long operation time due to a large number of layers and the deep network model. The operation time of the Vision Transformer is the longest except for InceptionE, which is due to its larger number of parameters and floating point operations, second only to InceptionE, indicating that the application of Vision Transformer in the field of computer vision still has a certain optimization space in terms of model lightweight and improving computational efficiency.  
Test Results and Analysis of the Refined Deep Learning Models for the Identification of Slope Surface Cracks