# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Classifier + Loss Function**} & \multicolumn{3}{c}{**Learning Rate (Accuracy)**} & \multicolumn{3}{c}{**Learning Rate (Loss)**} \\ \cline{3-8}  & & & **0.001** & **0.0001** & **0.00001** & **0.0001** & **0.00001** \\ \hline \multirow{8}{*}{\begin{tabular}{c} Fashion- \\ MNIST \\ \end{tabular} } & \multirow{4}{*}{ResNet18} & CASVM (hinge) & 0.93 & 0.94 & 0.94 & 0.04 & 0.03 & 0.03 \\  & & CASVM (squared hinge) & 0.93 & 0.94 & 0.94 & 0.05 & 0.06 & 0.06 \\  & & CASVM (categorical hinge) & 0.92 & 0.92 & 0.93 & 0.23 & 0.23 & 0.24 \\  & & SVM + hinge & 0.92 & 0.94 & 0.94 & 0.04 & 0.03 & 0.03 \\  & & SVM + squared hinge & 0.92 & 0.94 & 0.93 & 0.06 & 0.07 & 0.07 \\  & & SVM + categorical hinge & 0.93 & 0.93 & 0.92 & 0.18 & 0.21 & 0.22 \\  & & softmax + CA & 0.91 & 0.91 & 0.90 & 0.23 & 0.28 & 0.29 \\  & & softmax & 0.92 & 0.90 & 0.88 & 0.22 & 0.35 & 0.35 \\ \hline \multirow{8}{*}{\begin{tabular}{c} Cifar10 \\ \end{tabular} } & \multirow{4}{*}{AlexNet} & CASVM (hinge) & 0.75 & 0.83 & 0.83 & 0.11 & 0.09 & 0.10 \\  & & CASVM (squared hinge) & 0.75 & 0.83 & 0.79 & 0.15 & 0.20 & 0.20 \\  & & CASVM (categorical hinge) & 0.77 & 0.79 & 0.80 & 0.52 & 0.57 & 0.57 \\  & & SVM + hinge & 0.76 & 0.83 & 0.83 & 0.10 & 0.10 & 0.10 \\  & & SVM + squared hinge & 0.78 & 0.79 & 0.75 & 0.17 & 0.20 & 0.21 \\  & & SVM + categorical hinge & 0.74 & 0.79 & 0.78 & 0.59 & 0.56 & 0.60 \\  & & softmax + CA & 0.78 & 0.74 & 0.74 & 0.83 & 0.75 & 0.80 \\  & & softmax & 0.76 & 0.75 & 0.74 & 0.72 & 0.76 & 0.81 \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Cifar10 \\ \end{tabular} } & \multirow{4}{*}{ResNet18} & CASVM (hinge) & 0.72 & 0.76 & 0.79 & 0.11 & 0.10 & 0.09 \\  & & CASVM (squared hinge) & 0.74 & 0.76 & 0.76 & 0.16 & 0.17 & 0.17 \\  & & CASVM (categorical hinge) & 0.74 & 0.74 & 0.74 & 0.53 & 0.53 & 0.54 \\  & & SVM + hinge & 0.70 & 0.76 & 0.77 & 0.11 & 0.09 & 0.09 \\  & & SVM + squared hinge & 0.74 & 0.77 & 0.77 & 0.15 & 0.14 & 0.15 \\  & & SVM + categorical hinge & 0.71 & 0.72 & 0.73 & 0.52 & 0.56 & 0.51 \\  & & softmax + CA & 0.70 & 0.62 & 0.59 & 0.95 & 1.10 & 1.17 \\  & & softmax & 0.19 & 0.62 & 0.48 & 0.98 & 0.67 & 0.94 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Cifar100 \\ \end{tabular} } & \multirow{4}{*}{ResNet18} & CASVM (hinge) & 0.06 & 0.40 & 0.55 & 0.02 & 0.02 & 0.02 \\  & & CASVM (squared hinge) & 0.52 & 0.52 & 0.56 & 0.03 & 0.03 & 0.03 \\  & & CASVM (categorical hinge) & 0.22 & 0.50 & 0.50 & 0.88 & 1.41 & 1.45 \\  & & SVM + hinge & 0.03 & 0.57 & 0.58 & 0.02 & 0.02 & 0.02 \\  & & SVM + squared hinge & 0.47 & 0.58 & 0.57 & 0.03 & 0.03 & 0.03 \\  & & SVM + categorical hinge & 0.49 & 0.51 & 0.53 & 0.85 & 0.94 & 1.17 \\  & & softmax + CA & 0.43 & 0.42 & 0.41 & 2.21 & 2.27 & 2.32 \\  & & softmax & 0.50 & 0.35 & 0.32 & 1.92 & 2.59 & 2.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Image classification accuracy and loss values for small sizes. accuracies of 0.62, 0.84, and 0.92 were obtained at learning rates of 0.001, 0.0001, and 0.00001, respectively. The CASVM structure obtained the same accuracy as other structures in the experiment with 10 items, five on the Fashion-MNIST dataset, with an accuracy of 0.93 at a learning rate of 0.001, and an accuracy of 0.94 was obtained at learning rates of 0.0001 and 0.00001. With the Cifar10 dataset, the network model based on AlexNet was chosen to obtain an optimal accuracy of 0.83 at learning rates of 0.0001 and 0.00001. The network model based on Resnet18 was chosen to obtain an optimal accuracy of 0.74 at learning rates of 0.0001 and 0.001. From the above results, it is clear that CASVM obtained the most optimal classification accuracy for the entire experiment in the Animal10 dataset. The lowest loss values of CASVM in Tables 4 and 5 are less than 0.2, and the loss values are lower than 0.05 in the small Fashion-MNIST dataset. CASVM occupies 7 of the 12 lowest loss values for large Animal10 datasets.