# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 5 Discussions  
To validate the effectiveness of the model structure proposed in this paper, we compared previous research on combined SVM and CNN models. Table 8 summarizes the performance of these works in 10 training and testing sessions. Following the combination of CNN and SVM for image classification in the literature [3; 5], [PERSON] et al. [62] and [PERSON] et al. [63] conducted experiments for specific image classification with improved results. [PERSON] et al. [22] and [PERSON] et al. [43] have compared the classification results for both SVM and Softmax classifiers and obtained more significant results. Unlike previous works, the CASVM proposed in this paper uses a CA attention mechanism in the feature extraction module and employs a specific loss function. The CASVM framework can be easily ported into CNN-based models. According to our experiments, the highest accuracy is achieved using CASVM for image classification. Image classification accuracy is this paper's most critical performance metric, and Figure 8 presents the image classification accuracy obtained in 10 experiments on the Animal10 dataset. 500 samples will be accumulated for each image read in each of the 10 experiments, and 5000 samples will be read for each model in the last experiment. We used the feature extraction network from the previous work as the feature extraction module of CASVM to compare it with the model from the previous work. The relevant experimental parameters are set as in the previous work, and the other parameters are set according to Table 6. Among the models given by [PERSON] et al. [62], we choose the one that achieves the best results. Since the network of [PERSON] et al. [43] is shallow, the convolutional kernel size and the number of channels were adjusted appropriately to fit the input data. The parameters of the above-tuned models are synchronized to CASVM to ensure the validity of the comparison experiments. The results are shown in Figure 8 and Table 8. The accuracy and inference time for a single image in Table 8 are the average values after 10 tests, and the training time is the average length of training for one epoch. It can be seen from Figure 8 that the proposed model can maintain higher accuracy compared with other models, and CASVM has higher accuracy than other models in all 10 tests. Combined with Table 8, CASVM does not require much computation, except for a slightly slower training time, and has a higher classification accuracy than the other models. Among them, the highest accuracy improvement is 14%, while the number of parameters is only 0.003G higher, which again verifies that CASVM can obtain better performance with less computation. From the experimental time of each model, CASVM is slower than the other models in terms of training time, with the slowest time being 0.18 s, while the fastest is the same as this model. The inference time for a single image is even more similar. Taken together, the extra training time of CASVM is shorter, while the shorter training time can produce higher image classification accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FLOPs (G)** & **Accuracy** & **Training Time (s)** & **Testing Time (s)** \\ \hline [PERSON] et al. [5] & 1.949 & 0.77 & 15.69 & 0.55 \\ CASVM ([PERSON] et al.) & 1.951 & 0.83 & 15.87 & 0.57 \\ [PERSON] et al. [63] & 0.339 & 0.75 & 14.79 & 0.48 \\ CASVM ([PERSON] et al.) & 0.342 & 0.77 & 14.92 & 0.48 \\ [PERSON] et al. [62] & 0.928 & 0.66 & 15.27 & 0.54 \\ CASVM ([PERSON] et al.) & 0.929 & 0.67 & 15.29 & 0.55 \\ [PERSON] et al. [22] & 0.423 & 0.83 & 15.06 & 0.46 \\ CASVM ([PERSON] et al.) & 0.423 & 0.84 & 15.11 & 0.49 \\ [PERSON] et al. [43] & 0.198 & 0.65 & 14.84 & 0.51 \\ CASVM ([PERSON] et al.) & 0.201 & 0.79 & 14.96 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiments with different models.