# A Lightweight Module for Feature Fusion Based on Their Dimensions
## 5 Evaluation and Discussion  
This paper investigates the inter-group connection problem in lightweight models, which has been neglected. Several classic models are analyzed and an efficient information fusion module \(\varphi\)unit is proposed in this work, which can fuse the feature information from the current layer and the upper layer effectively, to alleviate the performance degradation problem caused by the discontinuity of feature information before and after the feature information fusion operation. The method achieves significant performance improvement with a small cost compared with traditional information fusion methods. In Section 4.1, this paper proves the stronger feature fusion and portability of \(\varphi\)unit compared with the traditional feature information fusion modules from the test results through a series of ablation experiments and comparative experiments; in Section 4.2, through the position exploration experiment of \(\varphi\)unit, it analyzes the different influences of the position of \(\varphi\)unit in the model on the model performance, and then determines the position relationship of \(\varphi\)unit in the model in the future practical application scenarios. Based on this, this experiment constructs \(\varphi\)Net on the basis of verifying the effectiveness of the proposed module and summarizing the previous work. Based on \(\varphi\)unit, we construct \(\varphi\)Net, which reduces the parameter size and computation cost by nearly half and 30%, respectively, compared with the reference network. \(\varphi\)Net can maintain high performance even when the number of classification categories increases, only 0.3% lower than the best model. This paper has room for further research on the relevant details of the model, such as the analysis of the influence degree of the number of repeated modules within each group on the model performance. This paper only explores one feasible scheme, and does not fully exploit the potential of \(\varphi\)Net. Subsequent research can find the optimal configuration through grid search or automated search algorithm, in order to significantly improve the performance of the improved model. In addition, parameter tuning and network structure search also need to be further explored. By applying parameter optimization algorithm and network structure search algorithm, the performance and generalization ability of the model can be further enhanced in this work. These studies will contribute to a deeper understanding of the influence of model structure and parameter configuration, and provide more optimization insights and guidance for advancing lightweight models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params. (M)** & **FLOPs. (M)** & **Acc. (\%)** \\ \hline Mobinetv3\_small & 2.54 & **59.81** & 60.50 \\ Squeezenet & **1.23** & 355.69 & 60.63 \\ Mobinetv3\_large & 5.48 & 227.71 & 66.57 \\ Ghost & 5.18 & 150.68 & 67.95 \\ GhostV2 & 6.15 & 178.59 & 68.00 \\ shufflnetv2\_x1.5 & 3.50 & 303.67 & 69.65 \\
**Ours** & 1.24 & 225.91 & 71.64 \\ Mobinetv2 & 2.22 & 318.96 & **71.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of classical lightweight models on food101 in terms of classification accuracy, parameter count, and computational complexity. **Author Contributions:** Conceptualization, Z.L. and R.Z. ; Designing experimental protocols, Z.L. and R.Z. ; data analyses, Y.L., P.R. and X.Y. writing--original draft preparation Z.L. and S.C.; supervision R.Z. writing--review and editing, Z.L., R.Z., Y.L. and P.R. ; revision of the article Y.L., P.R., X.Y. and S.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is supported by the National Key R&D Plan under Grant No. 2021 YFF0601200 and 2021 YFF0601204. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.