# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 5 Experimental Results
### Lightweight Model Analysis  
The number of model parameters is an important indicator of whether the model can be deployed to the mobile platform [39]. In order to reduce memory consumption and to improve the model recognition speed to meet the requirements for mobile applications, it is necessary to minimize the amount of model parameters and FLOPs (floating point operations). We designed a compression experiment using the same dataset and experimental methods, inspired by the network compression method of MobileNets [39], we compressed SE_Xception by reducing the number of depth-wise separable convolution blocks, and also reducing the width of the convolution layers in the model by multiply \(\alpha\) to the width of the input channels [39]. We designed several lightweight models, and compared their average recognition accuracy on ATLDD, number of parameters, and FLOPs. Firstly, in order to answer the question of whether to compress the width or the depth, we designed two networks have similar number of parameters. We removed the last six depth-wise separable convolutional blocks from SE_Xception to get the first network, we called this compressed network the SE_Xception_shallow. Then we designed the second network, named SE_Xception_0.75, by reducing the width of SE_Xception to 0.75-times those before. Table 2 compares the accuracy, number of parameters, and number of FLOPs of these two compressed networks and the baseline SE_Xception. We can see from Table 2 that these two compressed models have a similar number of parameters and FLOPs, but the accuracy of SE_Xception_0.75 only drops a little bit compared to SE_Xception, and is 1.47% higher than SE_Xception_shallow. Therefore, we have a conclusion that making SE_Xception thinner is better than making it shallower. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & **Number of** & \multirow{2}{*}{**FLOPs (M)**} & **Accuracy** \\  & **Parameters (M)** & & **(\%)** \\ \hline SE\_Xception & 23.85 & 48.15 & 99.40 \\ SE\_Xception\_shallow & 11.74 & 23.73 & 97.79 \\ SE\_Xception\_0.75 & 13.47 & 27.29 & 99.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of shallower and narrower SE_Xception. Figure 6: Comparison of convergence of SE_Xception and compared CNN models on the validation set. Secondly, since compressing width is better than compressing depth, we further compressed the width of SE_Xception with a scale of 0.25-times and 0.50-times to get SE_Xception_0.25 and SE_Xception_0.50. Then we compared these two networks with SE_Xception_0.75, baseline SE_Xception, MobileNetV1, and ShuffleNet. Table 3 shows the accuracy, number of parameters, and number of FLOPs of these models. The numbers of parameters and FLOPs drop off quickly, while the accuracy drops off smoothly until the width scale reaches 0.1. The identification accuracy of SE_Xception_0.25 is still higher than most of the compared networks in Table 1, although its numbers of parameters and FLOPs are far less than SE_Xception. Furthermore, the number of parameters of SE_Xception_0.25 is only 13.2% of that of SE_Xception_shallow, while its classification accuracy is 0.7% higher than that of SE_Xception_shallow. This implies that for the Xception structure in apple leaf disease identification task, the depth is more important than the width of the network. Furthermore, the number of parameter and FLOPs of SE_Xception_0.25 is less than those of MobileNetV1 and ShuffleNet, but its average classification accuracy is 3.08% and 2.70% higher than these two popular light weight networks. To further explore the possibility of compressing both depth and width of the network, we further delete six depth-wise separable convolution blocks from the latter part of SE_Xception_0.25, and name it as SE_Xception_0.25 shallow.