## 5 Results and Discussion
### Results of Model Optimization and Training
#### 5.3.2 Results of Improved DenseNet Feature Extraction Combined with SGD Classification  
Although the final accuracy of the classifier using SGD (with hinge loss as the loss function) was reduced, it showed significant advantages in classification efficiency. Specifically, the SGD classifier could achieve accuracy similar to more complex methods within fewer training epochs. This indicates that although there is some loss in final accuracy, it is acceptable because it results in a substantial reduction in overall training time.  
The study found that the rapid convergence property of the SGD classifier allows it to complete training in a shorter time. This is particularly important for applications that require rapid model iteration and deployment. For example, in tasks that demand real-time responses and quick decision making, the improved classification efficiency can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frozen Layers** & **Accuracy (\%)** & **Training Time (mins)** & **Epoch** \\ \hline
100\% & 89.84 & 58 & 13 \\
95\% & 90.88 & 60 & 16 \\
**90\%** & **90.99** & 57 & 12 \\
85\% & 90.57 & 40 & 10 \\
80\% & 90.07 & 29 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of improved DenseNet with frozen layers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **6 Questions Accuracy (\%)** & **12 Questions Accuracy (\%)** \\ \hline
[8] residual + concatenation & 82.26 & 69.7 \\
[8] residual + subtraction & 80.65 & 71.21 \\
[8] plain + concatenation & 79.03 & 74.24 \\
[8] plain + subtraction & 75.81 & 71.21 \\
**Improved DenseNet** & **95.13** & **89.84** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of training improved DenseNet classification.  
significantly enhance the overall performance of the system. Although the decrease in accuracy is a non-negligible issue, this trade-off is reasonable and valuable when balancing efficiency and accuracy.  
Additionally, the SGD classifier also excels in resource utilization. With reduced computational resources and time required for training, the SGD classifier can achieve high classification performance under limited hardware conditions, which is a significant advantage for resource-constrained scenarios. Therefore, despite a slight decrease in accuracy, the SGD classifier still holds considerable application value due to its significant advantages in training efficiency and resource utilization.  
More importantly, the efficiency of the SGD classifier is not only reflected in the training process but also in the inference phase of actual applications. The model's simplicity results in faster inference speeds, making the SGD classifier more competitive in real-time applications. For example, in real-time data processing and instant decision-making systems, the efficient inference capability of the SGD classifier can significantly enhance system responsiveness and processing efficiency.  
In summary, while using the SGD stochastic gradient descent classifier (with hinge loss) for classification results in a reduction in final accuracy, it can substantially shorten overall training time by reducing the number of training epochs and resource consumption. This trade-off between efficiency and accuracy makes the SGD classifier still invaluable in specific application scenarios. However, since accuracy is the most crucial criterion for this task, the next stage of software application implementation will omit SGD as the classifier and use the model with 90% of the improved DenseNet training frozen, applying this model for classification. Table 7 shows the experimental results, with the combination of SGD recording the time taken for feature extraction and classification separately.