# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
As can be seen in Tables 4 and 5, CNN + SVM without CA embedding has five items with the best classification accuracy among the eight sets of image classification metrics. One was obtained when the Cifar10 dataset was selected, i.e., the network model based on ResNet18, with an accuracy of 0.77 when the learning rate was 0.0001. Two were obtained when the Cifar100 dataset was selected, i.e., the network model based on ResNet18, with learning rates of 0.0001 and 0.00001, both with 0.58 accuracy. Two were obtained when the Animal10 dataset was selected, i.e., the network model based on CNN2 with learning rates of 0.0001 and 0.00001 and accuracies of 0.74 and 0.79. The CNN + SVM structure without embedded CA had 12 items with the same accuracy as other structures in the experiment. Among them, four were obtained with the Fashion-MNIST dataset. Values of 0.93 and 0.94 were obtained with learning rates of 0.001 and 0.00001, respectively, with the Resnet18 model; two optimal accuracies, both 0.94, were obtained with a learning rate of 0.0001. Four optimal identical accuracies were obtained when the Cifar10 dataset was selected. When the AlexNet-based network model was selected, accuracies of 0.78, 0.83, and 0.83 were obtained at learning rates of 0.001, 0.0001, and 0.00001, respectively; when the ResNet18-based network model was selected, an accuracy of 0.74 was obtained at the learning rate of 0.001. When the Cifar100 dataset was selected, two optimal identical accuracies of 0.58 were obtained with the ResNet18-based network model. When the Animal10 dataset was used, i.e., the CNN2-based network model, the accuracy was 0.79 at a learning rate of 0.00001. Figure 7: Output of feature maps by CA layers in different positions. The CNN + softmax structure had the best classification accuracy in only one of the eight sets of image classification metrics. The highest classification accuracy of 0.74 was obtained with the Animal10 dataset and the CNN2-based network model with a learning rate of 0.0001. CNN + softmax obtained the same accuracy as other experimental structures in the two items. Among them, one item was obtained by choosing the Cifar10 dataset, i.e., a network model based on AlexNet, with an accuracy of 0.78 at a learning rate of 0.001. One item was obtained with the Animal10 dataset, using a network model based on CNN2, with an accuracy of 0.74 at a learning rate of 0.0001. It can be seen from Tables 4 and 5 that the CASVM structure has 14 items, in order to obtain the best accuracy in the experiment. With the Cifar10 dataset, an accuracy of 0.79 was obtained at a learning rate of 0.00001 using the ResNet18-based network model. With the Cifar100 dataset, the ResNet18-based network model was chosen to obtain an accuracy of 0.52 at a learning rate of 0.001. The Animal10 dataset was chosen to obtain 12 items. With the CNN1-based network model, accuracies of 0.74 and 0.8 were obtained at learning rates of 0.001 and 0.00001, respectively, and both had an accuracy of 0.77 at a learning rate of 0.0001. With the network model based on CNN2, an accuracy of 0.65 was obtained at a learning rate of 0.001. With the network model based on ResNet18,