# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 4 Experiments
### Experiments on CIFAR-10 and CIFAR-100  
In this section, we adopt ResNet, SE-CNN and Res2-CNN as reference to verify the effectiveness of our proposed models from three aspects: Test accuracy, time complexity, and computational complexity. Among them, SE-CNN and Res2-CNN, respectively, add SE-blocks and Res2-blocks to the ResNet-32 structure. The structures of ResNet-32 and its various improved models are shown in Table 1. ResNet-32 consists of four convolutional blocks, a global average pooling layer, and a dense layer. There are two convolution operations in one residual block, in each square bracket. The three parameters of the convolution kernel are the number of feature channels after convolution, the sizes of the convolution kernels, and the strides. When dimension reduction is required, the strides are  
Figure 4: The schematic images of four datasets. equal to 2. The number of repetitions of each residual block is established according to the subsequent parameter \"_stack_n_\", which is 5, 4, or 1 in ResNet-32. As showed in Table 1, for CIFAR-10 and CIFAR-100, the number of convolution channels \(n\)1, \(n\)2, and \(n\)3 of ResNet-32 takes values of 16, 32, 64, and 32, 64, 128, respectively. Several other network models are implemented on the basis of ResNet-32. SE-CNN adds SE-block to each residual block of ResNet-32, adding a total of 15 SE-blocks. Considering the computational complexity and time complexity, we designed both \(A\) and \(B\) structures for the proposed methods. As showed in Table 1, Structure \(A\) adds a corresponding block after _Conv_2 and _Conv_3 of ResNet-32. Structure \(B\) replaces the underlined convolutional layers with the corresponding block. The corresponding blocks include AMS-block, DAMS-block and MAMS-block. For comparison purpose, Res2-CNN uses the same two architectures, such as Res2-CNN_A and Res2-CNN_B. Table 2 shows the test accuracy and time complexity of CIFAR-10 under different models. The parameter \"_Groups_\" indicate the number of grouping feature channels. The values in the experiments are 2, 4, and 8. Here ResNet and SE-CNN do not perform group convolution, which is commensurate with a value of 1. The parameter \"_SPE_\" means \"seconds per epoch\", which is the number of seconds required to execute each epoch. Owing to the different grouping methods, the parameters that need to be trained throughout the networks are also very different. For comparison, ResNet and SE-CNN adopt a 32-layer network structure, and other models adopt distinct layers to make the overall parameters quantity close to ResNet-32. For ResNet-32, the classification accuracy of CIFAR-10 is as high as 92.49% with only 0.46\(M\) parameter. SE-CNN introduces SE-block in the ResNet-32 architecture, which increased the test accuracy by 0.26%. Res2-CNN_A and Res2-CNN_B have improved the test accuracy by 0.28% and 0.39%, respectively, and their time complexity has gradually increased. Comparing different grouping forms of Res2-CNN, it can be concluded that the best result is achieved when the number of groups is 4. It can be seen from Table 2 that under the condition of only adding a small number of parameters, the classification accuracy of our proposed model is over 93%. Compared to ResNet, the test accuracy classification of AMS-CNN_A and AMS-CNN_B increased by 0.82% and 0.86%. Unlike Res2-CNN, the more channel groupings of AMS-CNN has, the better its performance, which mainly reflects the advantages of multi-scale functions. DAMS-CNN added spatial attention to AMS-CNN. The test accuracy classification of the two structures increased by 1.09% and 1.11%, and the highest result was 93.60%. Overall, the time complexity of several models corresponding to structure A is lower, while structure B takes more time. The main time consumption is in the process of sorting the channels by feature's importance. As can be seen from the FLOPs values, the computational complexities of the algorithms are on the same order of magnitude. This is because we control the size of the parameters of the network.