# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
The loss values of CNN + softmax structure in Animal10 are in the range of 0.99-2.25. Therefore, it can be concluded that CASVM can reduce the loss and better fit the CNN model based on the CASVM structure. Combining the accuracy results in Table 4 with the selection of each parameter in Section 4.1, the main training parameters for the given CASVM are shown in Table 6. \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{2}{c}{**Learning Rate (Accuracy)**} & \multicolumn{2}{c}{**Learning Rate (Loss)**} \\ \cline{3-8}  & & **0.001** & **0.0001** & **0.00001** & **0.001** & **0.0001** & **0.00001** \\ \hline \multirow{8}{*}{CNN1} & CASVM (hinge) & 0.63 & 0.73 & 0.79 & 0.16 & 0.13 & 0.11 \\  & CASVM (squared hinge) & 0.74 & 0.77 & 0.80 & 0.17 & 0.15 & 0.15 \\  & CASVM (categorical hinge) & 0.69 & 0.77 & 0.79 & 0.66 & 0.52 & 0.52 \\  & SVM + hinge & 0.44 & 0.62 & 0.67 & 0.20 & 0.16 & 0.14 \\  & SVM + squared hinge & 0.61 & 0.65 & 0.71 & 0.22 & 0.20 & 0.18 \\  & SVM + categorical hinge & 0.59 & 0.64 & 0.73 & 0.76 & 0.71 & 0.56 \\  & Softmax + CA & 0.18 & 0.55 & 0.29 & 2.25 & 1.31 & 1.89 \\  & Softmax & 0.53 & 0.41 & 0.32 & 1.38 & 1.70 & 1.88 \\ \hline \multirow{8}{*}{AlexNet} & CASVM (hinge) & 0.34 & 0.70 & 0.73 & 0.19 & 0.12 & 0.11 \\  & CASVM (squared hinge) & 0.66 & 0.69 & 0.73 & 0.19 & 0.18 & 0.16 \\  & CASVM (categorical hinge) & 0.60 & 0.66 & 0.65 & 0.72 & 0.62 & 0.67 \\  & SVM + hinge & 0.28 & 0.33 & 0.58 & 0.19 & 0.19 & 0.15 \\  & SVM + squared hinge & 0.37 & 0.50 & 0.57 & 0.31 & 0.26 & 0.23 \\  & SVM + categorical hinge & 0.19 & 0.46 & 0.53 & 0.97 & 0.83 & 0.77 \\  & Softmax + CA & 0.62 & 0.65 & 0.61 & 1.11 & 1.03 & 1.15 \\  & Softmax & 0.64 & 0.57 & 0.51 & 1.11 & 1.23 & 1.45 \\ \cline{2-8}  & CASVM (hinge) & 0.45 & 0.71 & 0.77 & 0.18 & 0.11 & 0.10 \\  & CASVM (squared hinge) & 0.64 & 0.73 & 0.77 & 0.20 & 0.17 & 0.15 \\  & CASVM (categorical hinge) & 0.65 & 0.70 & 0.76 & 0.58 & 0.53 & 0.50 \\  & SVM + hinge & 0.47 & 0.70 & 0.75 & 0.15 & 0.11 & 0.10 \\  & SVM + squared hinge & 0.62 & 0.72 & 0.78 & 0.20 & 0.17 & 0.17 \\  & SVM + categorical hinge & 0.63 & 0.65 & 0.79 & 0.61 & 0.62 & 0.41 \\  & Softmax + CA & 0.62 & 0.12 & 0.49 & 1.10 & 2.32 & 1.51 \\  & Softmax & 0.67 & 0.74 & 0.46 & 0.99 & 0.81 & 1.60 \\ \hline \multirow{8}{*}{ResNet18} & CASVM (hinge) & 0.33 & 0.81 & 0.92 & 0.21 & 0.10 & 0.05 \\  & CASVM (squared hinge) & 0.62 & 0.83 & 0.89 & 0.21 & 0.12 & 0.09 \\ \cline{1-1}  & CASVM (categorical hinge) & 0.62 & 0.84 & 0.87 & 0.76 & 0.39 & 0.33 \\ \cline{1-1}  & SVM + hinge & 0.21 & 0.22 & 0.79 & 0.20 & 0.21 & 0.13 \\ \cline{1-1}  & SVM + squared hinge & 0.31 & 0.63 & 0.85 & 0.35 & 0.25 & 0.11 \\ \cline{1-1}  & SVM + categorical hinge & 0.12 & 0.61 & 0.85 & 1.00 & 0.82 & 0.37 \\ \cline{1-1}  & Softmax + CA & 0.31 & 0.63 & 0.58 & 2.03 & 1.11 & 1.22 \\ \cline{1-1}  & Softmax & 0.45 & 0.56 & 0.66 & 1.79 & 1.32 & 1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Image classification accuracy and loss values for large sizes. As shown in Table 7, three CNN models with deeper layers were selected to verify the classification advantages of CASVM on large image datasets. It can be seen that CASVM performed well in terms of accuracy, reaching 99% and 93% on Fish9 and Animal10, respectively. In particular, CASVM (ResNet18) was 26% more accurate than the Resnet18 model. Combined with Table 2, it can be seen that CASVM outperformed the benchmark model in terms of accuracy and loss value, its computational and parametric quantities were close to those of the benchmark model, and the difference in running time was slight, which demonstrates its better robustness and avoidance of significant computational overhead. As the average of accuracy and recall coordination, F1-scores of up to 98% demonstrate the good classification performance of CASVM on large images.