# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 4 Experiments
### Experiments on Fine-Grained Image Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Conv1** & **Conv2** & **Conv3** & **Conv4** & **Conv5** & **Conv6** & **Dence** \\ \hline \multirow{5}{*}{ResNet} & (16, 7, 2) & \(\left\{48,3\times 3\right\}\) & \(\left\{64,3\times 3\right\}\) & \(\left\{64,3\times 3\right\}\) & \(\left\{128,3\times 3\right\}\) & \(\left\{192,3\times 3\right\}\) & \(\left\{192,3\times 3\right\}\) & \(\left\{256,3\times 3\right\}\) \\  & (16, 5, 2) & \(\left\{48,3\times 1\right\}\) & \(\times\) & \(\left\{64,1\times 3\right\}\) & \(\times\) & \(\left\{128,1\times 3\right\}\) & \(\times\) & \(\left\{192,1\times 3\right\}\) \\  & (16, 3, 2) & \(\left\{48,1\times 3\right\}\) & \(\left\{64,1\times 3\right\}\) & \(\left\{128,3\times 3\right\}\) & \(\left\{192,3\times 3,2\right\}\) & \(\left\{256,3\times 3,2\right\}\) & \(\left\{256,3\times 1\right\}\) & \(\times\) 5 \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \multicolumn{6}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{3-6} \cline{7-7} \multicolumn{1}{c}{SE-CNN} & \multicolumn{1}{c}{–} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\ Res2-CNN & \multicolumn{1}{c}{–} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & \\ Res2-block & \multicolumn{1}{c}{Res2-block} & \multicolumn{1}{c}{Res2-block} & \multicolumn{1}{c}{Res2-block} & \multicolumn{1}{c}{Res2-block} & \multicolumn{1}{c}{} & \\ AMS-CNN & AMS-block & AMS-block & AMS-block & AMS-block & AMS-block & \\ DAMS-CNN & DAMS-block & DAMS-block & DAMS-block & DAMS-block & \\ MAMS-CNN & MAMS-block & MAMS-block & MAMS-block & MAMS-block & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The simple structures of several convolutional networks with residual module. The parameters of the networks are shown in Tables 5 and 6, and the values are between \(10M\) and \(11M\). Since the datasets FGVC-Aircraft and Stanford Cars, respectively, contain images with 100 and 196 categories, the final output nodes of the networks are different. Therefore, the amount of parameters in Table 6 is slightly higher overall than Table 5. For FGVC-Aircraft, the test accuracies of MAMS-CNN reached 86.56%, which was 3.51%, 2.34%, and 4.29% higher than ResNet, SE-CNN, and Res2-CNN, respectively. At the same time, the performance of multi-scale networks with single attention, dual attention and multiple attention is getting higher and higher, indicating that the attention mechanisms have a natural advantage in image classification. It is worth noting that Res2-CNN does not show good performance on FGVC-Aircraft. For Stanford Cars, the MAMS-CNN network structure also achieved the best results, with a test accuracy rate of 89.15%, which is 6.13%, 6.06%, and 5.53% higher than ResNet, SE-CNN, and Res2-CNN, respectively. Even compared to the AMS-CNN and DAMS-CNN models, their performance is also improved by 0.50% and 0.13%, respectively. On the other hand, it can be seen that the more complicated the network structure is, the more time is consumed. However, for a dataset with an overall number of more than 10,000 images, the time complexity is within the tolerable range. Comparing two fine-grained datasets, we can see that our proposed models have better improvement effect on Stanford Cars than FGVC-Aircraft, because the former has more local features for fine-grained image recognition than the latter. As can be seen from the FLOPs and SPE, under similar computational complexity conditions, the time complexities are significantly different because the Channels-Sorting consumes a lot of time. As can be seen in Figure 7, from AMS-CNN to MAMS-CNN, the performance of the test loss is more stable. Comparing the three structures on two different datasets, it can be seen that the test loss on Stanford Cars are much less fluctuating and the final loss values are smaller. This is consistent with the performance of the networks on both datasets.