# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 4 Experiments
### Experiments on CIFAR-10 and CIFAR-100
As can be seen from Table 3, when using _mode_A_ training for various networks, the number of seconds it takes to process the images of the entire dataset is almost the same. However, when using the _mode_B_, the time complexity will increase a lot. For example, the _SPE_ value of DAMS-CNN_B is more than six times that of ResNet-32. Under the same conditions, the performance of the three models we proposed exceeding the capabilities of Res2-CNN, SE-CNN, and ResNet. For the CIFAR-100, we achieved a maximum test accuracy of 75.87%, which was 2.78%, 1.68%, and 2.39% higher than the previous three comparison models. In addition, the performance of AMS-CNN_B is higher than that of AMS-CNN_A, which shows that adopting more AMS-block is beneficial to the improvement of network performance. Moreover, the performance of DAMS-CNN_A is better than that of AMS-CNN_A, indicating that the introduction of spatial attention mechanism on the basis of the latter is beneficial to feature learning. However, the overall performance of DAMS-CNN_B is not higher than DAMS-CNN_A. Through analysis, we believe that the network has been over-fitted. Therefore, we have not discussed the more complex model (MAMS-CNN) for the basic CIFAR dataset. Similar to the previous experiments, the computational complexities of the algorithms are on the same order of magnitude. Similar to Figure 5, structure B in Figure 6 is more efficient than the structure A, while DAMS-CNN is better than AMS-CNN. The overall loss of CIFAR-100 is large and fluctuates greatly during training. This happens because the image classification of the FIFAR-100 is more complex. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Groups** & **Accuracy (\%)** & **Parameters** & **SPE** & **FLOPs** \\ \hline ResNet-32 & 1 & 73.09 & 1.87\(M\) & 32 & 7.50 \(\times\) 10\({}^{6}\) \\ SE-CNN & 1 & 74.19 & 1.90\(M\) & 42 & 7.61 \(\times\) 10\({}^{6}\) \\  & 2 & 73.47 & 1.96\(M\) & 36 & 7.85 \(\times\) 10\({}^{6}\) \\ Res2-CNN\_A & 4 & 73.48 & 1.95\(M\) & 37 & 7.80 \(\times\) 10\({}^{6}\) \\  & 8 & 74.19 & 1.94\(M\) & 39 & 7.75 \(\times\) 10\({}^{6}\) \\  & 2 & 74.35 & 1.95\(M\) & 80 & 7.81 \(\times\) 10\({}^{6}\) \\ Res2-CNN\_B & 4 & 74.35 & 1.86\(M\) & 94 & 7.47 \(\times\) 10\({}^{6}\) \\  & 8 & 75.17 & 1.76\(M\) & 117 & 7.05 \(\times\) 10\({}^{6}\) \\  & 2 & 75.32 & 2.01\(M\) & 40 & 8.06 \(\times\) 10\({}^{6}\) \\ AMS-CNN\_A & 4 & 75.25 & 1.97\(M\) & 42 & 7.88 \(\times\) 10\({}^{6}\) \\  & 8 & 75.36 & 1.95\(M\) & 47 & 7.78 \(\times\) 10\({}^{6}\) \\  & 2 & 75.31 & 1.98\(M\) & 111 & 7.96 \(\times\) 10\({}^{6}\) \\ AMS-CNN\_B & 4 & 75.87 & 1.98\(M\) & 154 & 6.80 \(\times\) 10\({}^{6}\) \\  & 8 & 75.42 & 1.82\(M\) & 201 & 6.22 \(\times\) 10\({}^{6}\) \\  & 2 & 75.52 & 1.99\(M\) & 38 & 7.99 \(\times\) 10\({}^{6}\) \\ DAMS-CNN\_A & 4 & 75.34 & 1.97\(M\) & 43 & 7.80 \(\times\) 10\({}^{6}\) \\  & 8 & 75.25 & 1.94\(M\) & 48 & 7.71 \(\times\) 10\({}^{6}\) \\  & 2 & 75.41 & 1.99\(M\) & 125 & 7.97 \(\times\) 10\({}^{6}\) \\ DAMS-CNN\_B & 4 & 75.38 & 1.99\(M\) & 167 & 6.81 \(\times\) 10\({}^{6}\) \\  & 8 & 75.43 & 1.82\(M\) & 216 & 6.23 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The FLOPs and SPE (seconds per epoch) of the residual networks carrying different convolution models, and the classification accuracies of these networks on the CIFAR-100 dataset.