## 4 Experiments
### Grouping Attention Model  
The focus of this section is on several grouping attention residual networks. The several convolutional networks in Table 3 introduce a group attention model (GA-model) into the Res-CNN architecture. This model consists of a group attention block (GA-block) and a group convolution block \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy(\%)**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{4-5}  & **Cars** & & & \\ \hline B-CNN & 83.90 & 78.40 & – & 8 \\ Res-CNN & 84.29 & 82.15 & 14.09M & 40 \\ OARes-CNN & 84.87 & 82.65 & 15.78M & 38 \\ PARes-CNN & 84.80 & 82.62 & 14.10M & 38 \\ TA-CNN\_ADD & 85.10 & 82.94 & 15.78M & 36 \\ TA-CNN\_SORT & 85.22 & 83.01 & 15.78M & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters and frames per second (FPS) of the residual networks carrying different attention models, and the classification accuracies of these networks on the two datasets. (GC-block). Their main structures are shown in GA-CNN in Table 1, and the GA-block operation is executed after _Conv_5; that is, these feature channels with high semantics are divided into multiple groups. As shown in Table 3, we divided them into 1 group, 4 groups, 8 groups, 12 groups, and \(X\) groups (\(X\) indicates the number of feature channels, and the value in this experiment was 192). Then, we performed a GC-block operation on each group, and the number of feature channels after the group convolution in the experiment was still 192. It should be noted here that replacing _Conv_6 with a GC-block will greatly reduce the network parameters. In order to compare network performance fairly, we performed a traditional convolution operation on the feature maps before group convolution to obtain 128 feature channels. These two types of feature maps were then merged for the final image classification tasks. Res-CNN treats the feature maps after _Conv_5 as one group, which is Res-CNN_G1. As can be seen from Table 3, as the groups increase, the amount of parameters slowly decreases, while the time complexity increases gradually. This is because when the group convolution operation is performed, the group increases, and the number of channels per group decreases, resulting in a decrease of parameters. The extra time is mainly consumed in the process of grouping. Res-CNN_GX is an exception, and its time complexity is similar to that of Res-CNN, mainly because we adopt depth-wise convolution operations to improve network efficiency. For the Cars and Air datasets, the classification accuracies were the highest in the 8 groups, reaching 89.51% and 87.06%, respectively. These show that the two datasets can achieve more accurate classification when obtaining about 8 discriminative local features. In addition, Res-CNN_GX achieved better results than Res-CNN after introducing additional traditional convolution operations. This illustrates that the use of grouping attention at the advanced semantics layer helps us to improve network classification performance. We define Res-CNN_G8 as GA-CNN. Compared with Res-CNN, the classification accuracies of GA-CNN on the datasets Stanford Cars and FGVC-Aircraft increased by 5.22% and 4.91%, respectively. In the previous experiment, we improved the Res-CNN with a two-level attention model or a grouping attention model, respectively. Naturally, we will embed both two-level attention model and grouping attention model into the Res-CNN architecture; that is, introduce grouping attention on the TA-CNN. As shown in TGA-CNN in Table 1, we replaced _Conv_6 with a GA-model (GA-block and GC-block), and the rest of the operation was the same as mentioned earlier. TA-CNN is TA-CNN_SORT in Table 2, and the feature maps after _Conv_5 can be interpreted as only one group. TA-CNN_GX indicates that each feature channel of the corresponding feature maps is divided into one group. As TA-CNN_GX employs depth-wise convolution operations, its parameters and time complexity are smaller.