## 4 Experiments and Results
### Experimental Setting  
Table 1 shows the architecture details of the MFN model on the SAR military vehicle dataset. Input images with a resolution of 128 \(\times\) 128 were simultaneously processed by the CovNeXt-SimAM and Swin Transformer branches. Each branch was divided into three stages to obtain local and global features at different scales. Subsequently, fusion features containing rich SAR feature information were obtained by a multi-scale feature fusion branch. After global average pooling, SAR features were classified by a fully connected layer.  
The details on the MFN model parameters are provided in Table 1. In the convolution layer parameters, d7 \(\times\) 7, 96 is the short form of the output, where d represents the group convolution, 7 \(\times\) 7 represents the convolution kernel size, and 96 represents the number of channels. Avgpool k2, s4 indicates an average pooled operation with a pool window size of 2 \(\times\) 2 and a step size of 4. Parameters in the Swin Transformer branch include window size = 7 \(\times\) 7 and head = 3, which indicate a multi-head self-attention mechanism with a window size of 7 \(\times\) 7 and three heads.  
To evaluate the performance of the proposed model, it was tested on a computer with AMD Ryzen 7-5800 CPU, NVIDIA GeForce RTX 3060 GPU, and 16 GB memory. The deep learning frameworks of Python 3.6.6 and PyTorch-1.7.1 were used. In this study, we used label smoothing as the loss function and optimized it using the AdamW gradient descent algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Output Size** & **CovNeXt-SimAM Branch** & **Multi-Scale Feature Fusion Branch** & **Swin Transformer Branch** \\ \hline - & 32 \(\times\) 32, 96 & 4 \(\times\) 4, 96 & - & 4 \(\times\) 4, 96 \\ \hline Stage 1 & 32 \(\times\) 32, 96 & \begin{tabular}{c} [\begin{array}{c}\text{d7 $\times$ 7, 96}\\ 1 $\times$ 1, 384 \\ 1 $\times$ 1, 96 \\ \end{array}\] \(\times\) 2 \\ \end{tabular} & \(\rightarrow\) 1 \(\times\) 1, 96 \(\leftarrow\) & \begin{tabular}{c} [\begin{array}{c}window size=7 $\times$ 7\\ \text{head}=3,\\ 1$\times$ 1, 96 \\ \end{array}\] \(\times\) 2 \\ \end{tabular} \\ \hline Stage 2 & 16 \(\times\) 16, 192 & \begin{tabular}{c} [\begin{array}{c}\text{d7 $\times$ 7, 192}\\ 1 $\times$ 1, 1768 \\ 1 $\times$ 1, 192 \\ \end{array}\] \(\times\) 2 \\ \end{tabular} & \(\rightarrow\) 2 & \begin{tabular}{c} Avgpool k2, s4 \\ \(\downarrow\) \\ \end{tabular} & \(\begin{bmatrix}window size=7 $\times$ 7\\ \text{head}=6,\\ 1$\times$ 1, 192 \\ \end{bmatrix}\) \(\times\) 2 \\ \hline Stage 3 & 8 \(\times\) 8, 384 & \
\begin{tabular}{c} [\begin{array}{c}\text{d7 $\times$ 7, 384}\\ 1$\times$ 1, 1536\\ 1$\times$ 1, 384 \\ \end{array}\] \(\times\) 2 \\ \end{tabular} & \(\rightarrow\) 1 \(\times\) 1, 384 \(\leftarrow\) &
\begin{tabular}{c} [\begin{array}{c}window size=7 $\times$ 7\\ \text{head}=12,\\ 1$\times$ 1, 384 \\ \end{array}\] \(\times\) 2 \\ \end{tabular} \\ \hline Classifier & \(1\times 1,1\) & - & \multicolumn{2}{c}{global average pooling} & - \\ \cline{3-5}  & & \multicolumn{2}{c}{\(1\times 1,\) numclass} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific parameters of MFN model (in the multi-scale feature fusion branch, the arrow indicates the direction of the feature).