## 4 Results  
VGG16 is employed as a fixed feature extractor, with the stacked layers subsequently trained on new datasets. Subsequently, to enhance the network performance, certain layers of the base model are unfrozen, allowing them to be updated during training. In neural network architecture, weights and biases collectively constitute the parameters. Parameters that remain fixed and unaltered during the training process are referred to as non-trainable parameters. Table 1 delineates the number of trainable and non-trainable parameters for the customized model. A customized model is trained and validated by tuning the learning rate parameter and the number of epochs. The testing and validation accuracies of the trained models are shown in Table 2. The total running time is 484.59 s.  
From the above table, it is evident that the accuracy rate increases as the number of epochs increases for the same network. Additionally, there exists a relationship between the training accuracy and the learning rate. The graphs (Figure 8) depicting the training loss, validation loss, training accuracy, and validation accuracy are crucial for evaluating the model's performance during training. A decrease in the training loss signifies improved learning and levels of efficacy for the training data, while a validation loss indicates the model's ability to generalize with new data. The training accuracy shows how well the model classifies or predicts the training data, typically improving over time. The validation accuracy reflects the model's generalization ability for unseen data; stability or an improvement throughout training suggests it has an effective generalization ability and prevents overfitting. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Total Parameters** & **Trainable** & **Non-Trainable** \\ \hline Customized Model & 39,805,690 & 25,091,002 & 14,714,688 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Trainable and non-trainable parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Epoch** & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Loss** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Validation** \\ **Loss** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} \\ \hline Epoch 1/10 & 15 s 3 s/step & 6.2137 & 0.5000 & 6.2114 & 0.5000 \\ \hline Epoch 2/10 & 16 s 3 s/step & 2.0953 & 0.5217 & 2.1691 & 0.5100 \\ \hline Epoch 3/10 & 15 s 3 s/step & 1.7656 & 0.5669 & 1.7505 & 0.5500 \\ \hline Epoch 4/10 & 16 s 3 s/step & 0.8504 & 0.7587 & 0.9814 & 0.7300 \\ \hline Epoch 5/10 & 15 s 3 s/step & 0.9370 & 0.7054 & 0.7952 & 0.7400 \\ \hline Epoch 6/10 & 16 s 3 s/step & 0.6226 & 0.7319 & 0.6401 & 0.7100 \\ \hline Epoch 7/10 & 15 s 3 s/step & 0.6660 & 0.7668 & 0.7453 & 0.7400 \\ \hline Epoch 8/10 & 15 s 3 s/step & 0.5976 & 0.7720 & 0.6835 & 0.7400 \\ \hline Epoch 9/10 & 20 s 5 s/step & 0.5129 & 0.7398 & 0.5192 & 0.7400 \\ \hline Epoch 10/10 & 16 s 3 s/step & 0.6034 & 0.7305 & 0.6018 & 0.7300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The testing and validation accuracies of the trained models. The performance of the classification model was evaluated using its precision, recall, F1 score, and accuracy metrics (Table 3). While the overall accuracy of the model was over 70%, slightly surpassing that of making random guesses, there remains room for improvement, suggesting opportunities for enhancing model performance. The evaluation was conducted on an independent test set, ensuring the model's performance was assessed on unseen data, enhancing its reliability in real-world applications. Additionally, macro and weighted averages for its precision, recall, and F1 score were consistent with individual class metrics, providing a comprehensive overview of the model's performance across all categories.