# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 4 Experiments
### Experiments on CIFAR-10 and CIFAR-100
Figure 5 shows the trend of changes in the test loss of CIFAR-10 for various network models. The downward tendency of structure B is more obvious than that of structure A, indicating \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Conv1** & **Conv2** & **Conv3** & **Conv4** & **Dence** \\ \hline ResNet-32 & (n1, 3 \(\times\) 3, 1) & \(\left[\begin{array}{c}\text{n1, 3}\times\text{3, 1}\\ \text{\emph{n2, 3}}\times\text{3, 1}\end{array}\right]\times 5\) & \(\left[\begin{array}{c}\text{n2, 3}\times\text{3, 1}\\ \text{\emph{n3, 3}}\times\text{3, 1}\end{array}\right]\times 4\) & \(\left[\begin{array}{c}\text{n3, 3}\times\text{3, 1}\\ \text{\emph{n3, 3}}\times\text{3, 1}\end{array}\right]\times 4\) & \(\left[\begin{array}{c}\text{n3, 3}\times\text{3, 1}\\ \text{\emph{n3, 3}}\times\text{3, 1}\end{array}\right]\times 4\) & \\ \(\left[\begin{array}{c}\text{n2, 3}\times\text{3, 1}\end{array}\right]\times 1\) & \(\left[\begin{array}{c}\text{n2, 3}\times\text{3, 2}\\ \text{\emph{n3, 3}}\times\text{3, 1}\end{array}\right]\times 1\) & GAP (8,8) \\ SE-CNN & - & - & (10/100) \\ Res2-CNN\_A/B & Res2-block & Res2-block & \\ AMS-CNN\_A/B & AMS-block & AMS-block & \\ DAMS-CNN\_A/B & DAMS-block & DAMS-block & \\ MAMS-CNN\_A/B & MAMS-block & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The simple structures of several convolutional networks with residual module. that the more AMS-block or DAMS-block is used, the more powerful the network is. The overall performance of DAMS-CNN is better than AMS-CNN. We adopt ResNet-32 as the network foundation model and limit the network parameters around 1.9\(M\). Table 3 shows the test accuracies and time consumption of ResNet-32 and its various deformation models on the CIFAR-100. As before, we add an SE-block to each residual block of ResNet-32 to form SE-CNN. In addition, Res2-CNN, AMS-CNN and DAMS-CNN employ both A and B structures. At the same time, we have different groupings for the proposed models. We did not use the MAMS-CNN model for the CIFAR-10 and CIFAR-100 datasets. This is because the CIFAR datasets is relatively simple, and an overly complex feature learning model can lead to overfitting. It is worth noting that when we train AMS-CNN and DAMS-CNN, we increase the network weight attenuation coefficient \"_weight_decay_\" from 0.0001 to 0.0005, in order to avoid over-fitting caused by complex models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Groups** & **Accuracy (\%)** & **Parameters** & **SPEPS** & **FLOPs** \\ \hline ResNet-32 & 1 & 92.49 & 0.46\(M\) & 22 & \(1.87\times 10^{6}\) \\ SE-CNN-32 & 1 & 92.75 & 0.47\(M\) & 29 & \(1.90\times 10^{6}\) \\  & 2 & 92.64 & 0.49\(M\) & 26 & \(1.96\times 10^{6}\) \\ Res2-CNN\_A & 4 & 92.77 & 0.49\(M\) & 27 & \(1.95\times 10^{6}\) \\  & 8 & 92.25 & 0.48\(M\) & 29 & \(1.94\times 10^{6}\) \\  & 2 & 92.77 & 0.49\(M\) & 55 & \(1.97\times 10^{6}\) \\ Res2-CNN\_B & 4 & 92.88 & 0.47\(M\) & 65 & \(1.88\times 10^{6}\) \\  & 8 & 92.82 & 0.44\(M\) & 90 & \(1.78\times 10^{6}\) \\  & 2 & 93.16 & 0.50\(M\) & 29 & \(2.01\times 10^{6}\) \\ AMS-CNN\_A & 4 & 93.13 & 0.49\(M\) & 33 & \(1.97\times 10^{6}\) \\  & 8 & 93.31 & 0.49\(M\) & 37 & \(1.95\times 10^{6}\) \\  & 2 & 93.18 & 0.50\(M\) & 80 & \(2.01\times 10^{6}\) \\ AMS-CNN\_B & 4 & 93.14 & 0.50\(M\) & 112 & \(1.72\times 10^{6}\) \\  & 8 & 93.35 & 0.46\(M\) & 169 & \(1.57\times 10^{6}\) \\  & 2 & 93.58 & 0.50\(M\) & 31 & \(2.02\times 10^{6}\) \\ DAMS-CNN\_A & 4 & 93.39 & 0.49\(M\) & 33 & \(1.97\times 10^{6}\) \\  & 8 & 93.41 & 0.49\(M\) & 37 & \(1.95\times 10^{6}\) \\  & 2 & 93.33 & 0.50\(M\) & 91 & \(2.01\times 10^{6}\) \\ DAMS-CNN\_B & 4 & 93.39 & 0.50\(M\) & 128 & \(1.72\times 10^{6}\) \\  & 8 & 93.60 & 0.46\(M\) & 185 & \(1.58\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The FLOPs (floating point operations) and SPE (seconds per epoch) of the residual networks carrying different convolution models, and the classification accuracies of these networks on the CIFAR-10 dataset. Figure 5: Test loss of various network structures on CIFAR-10.