## 4 Results and Discussion
### Comparative Experiments of Different Models on our Own Dataset
#### 4.1.2 Comparison Experiments of ResNet and SE-ResNeXt in Different Numbers of Layers  
Based on our own dataset, ResNet and SE-ResNeXt with layers of 50, 101, and 152 were used for comparison experiments. The experimental learning rates in this section were set to 0.1 and 0.01, respectively. The comparison work is conducted by analyzing the training results from two groups of experiments. One group is set with the same number of layers under different structures. The other is set with different numbers of layers under the same structure. Figure 6 shows the results of the comparison experiments where the number of layers is different.  
As shown in the above figures, the maximum training accuracy of ResNet is approximately 83.6%, regardless of the number of layers, which is the same as the SE-ResNeXt with different layers. The difference is that the maximum accuracy of SE-ResNeXt training is higher than ResNet, which is close to 90.6%, indicating that the training of SE-ResNeXt has better recognition performance. Even if the final accuracy is similar, the training process is different for models with different layers. Before 70 epochs, the higher the number of layers, the lower the accuracy of training when the number of iterations is the same. This is because the model has more parameters when the number of layers increases, so it needs more iterations to obtain higher precision.  
Figures 7 and 8 show the training accuracy variations with epoch changes when the learning rates are 0.1 and 0.01, respectively. When the learning rate is 0.1, the train accuracy of SE-ResNeXt is higher than that of ResNet throughout the training process, regardless of the number of layers. Moreover, as the epoch increases, the difference in accuracy also increases. When the learning rate is reduced to 0.01, the difference decreases. In the process of training, there is almost no difference  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **Red Deer** & **Goral** & **Roe Deer** & **Lynx** & **Badger** \\ \hline Daytime & 2822 & 1027 & 1698 & 301 & 292 \\ Dark & 1782 & 639 & 839 & 120 & 146 \\ Number of Images & 4604 & 1666 & 2537 & 421 & 438 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wildlife image sample database.  
Figure 6: Variations of train accuracy with epoch changes (lr = 0.1). (**a**) The model of ResNet; (**b**) The model of SE-ResNeXt.  
between the SE-ResNeXt and the ResNet when the number of layers is 50. As the number of layers increases, the difference between SE-ResNeXt and ResNet increases marginally.  
Table 3 displays the test accuracy of the six models in the two sets of experiments. It can be observed that the accuracy of 0.01 learning rate is higher than that of 0.1 learning rate for all six models. In addition, the learning rate has a significantly greater impact on ResNet than the impact on SE-ResNeXt. In two experiments with different learning rates, the accuracy differences of ResNet50 and ResNet152 both reached 2.7%, while the accuracy difference of SE-ResNeXt was only 1.6%. This shows that reducing the learning rate is beneficial for improving the recognition accuracy. At the same time, it can be seen intuitionally from the table that the test accuracy of SE-ResNeXt is always higher than that of ResNet when the models have the same number of layers. The maximum difference reaches 4.4%. From the table, it can be concluded that the training of SE-ResNeXtn101 achieves the best test results when the learning rate is 0.01.