# A Lightweight Module for Feature Fusion Based on Their Dimensions
## 4 Experiments and Result
### Module Position and Number Exploration  
As the arguments in the previous section demonstrated, the feature fusion module proposed in this paper had different performance effects in different positions of the model. Hence, in this section, this paper will carry out a series of investigations on the appropriate positions and numbers of the module to be added. Based on the architecture of MobiletV2, this paper determines the basic feature dimension information change of \"\(\varphi\)Net\" and inserts \(\varphi\)unit modules at the locations where the dimensions change. Next, the relevant modules are gradually reduced and the accuracy comparison analysis is performed until a model that can achieve a high effect is found, namely \(\varphi\)Net. Since the proposed model mainly relies on MobiletV2, we use MobiletV2 as a reference object for adjusting the model process. Figure 5 illustrates the trend of changes in the number of training iterations and accuracy resulting from different module locations in the experimental process, while Table 3 provides a detailed comparison of the experimental results. According to the experimental results of the relationship between \(\varphi\)unit distance and network endpoint distance in Section 4.1, when \(\varphi\)unit distance is too close to the network endpoint, the network tends to focus on shallow or deep feature information and ignore global feature information, thus reducing the final prediction accuracy of the model; Table 3 shows the results of the first and second experiments compared with the baseline model, which provide higher credibility for this theory. The orange and green curves in Figure 5 represent the results of the first and second experiments in Table 3, respectively. The blue curve shows the baseline model's corresponding experiments. The trend of the accuracy of the model during the training process indicates that: At the initial stage, the model focuses on extracting and analyzing shallow feature information, while the \(\varphi\)unit test model added near the network endpoint has a slight advantage over the baseline model in terms of accuracy. Generally, as the training number increases, the model will gradually focus more and more on the comprehensive analysis of the overall feature information. However, in the later stage of training, if the model pays too much attention to shallow or deep feature information and neglects the analysis of the overall feature information, it will eventually \begin{table}
\begin{tabular}{c c c} \hline \hline
**Erase** & **Acc. (\%)** & **Compare Model (Mobiletv2)** \\ \hline - & 72.31 & \\ First unit & 72.43 & **72.76** \\ First and Last unit & **75.31** & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy after removing different positional \(\varphi\)unit with Mobiletv2. lead to the model failing to maintain the growth trend after reaching a certain accuracy threshold. Figure 5 shows that the test model's accuracy in the later stage is lower than that of the baseline model. After removing the \(\varphi\)unit at the beginning and end of the model, this chapter further tested the improved model. Surprisingly, the test model significantly outperformed the MobiletV2 baseline model's accuracy performance, and whether it was in the training stage before or after training, the model could always surpass the baseline model in accuracy and maintain its leading position. The above results also indirectly confirmed two points: (1) the proposed \(\varphi\)unit was not effective at all positions, only at the intermediate position that balanced shallow and abstract features; (2) the experiment also verified again that the proposed \(\varphi\)unit had high information integration efficiency (still achieving superior experimental results under much fewer parameters than the baseline model). In conclusion, the model with the highest accuracy in the experiment was determined as \(\varphi\)Net.