## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**LRS** & **BS** & **EN** & \begin{tabular}{c} **TSP** \\ **(\%)** \\ \end{tabular} & **IS** & **PS** & **TB** & \
\begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & **P (\%)** &
\begin{tabular}{c} **R** \\ **(\%)** \\ \end{tabular} & **F1** \\ \hline \(\checkmark\) & 256 & 15 & 10 & 72 & 12 & 8 & 85.83 & 89.28 & 72.50 & 80.02 \\ \(\checkmark\) & 256 & 25 & 10 & 72 & 12 & 8 & 90.04 & 94.56 & 74.80 & 83.52 \\ \(\checkmark\) & 256 & 30 & 10 & 72 & 12 & 8 & 90.56 & 94.04 & 77.40 & 84.91 \\ \(\checkmark\) & 256 & 50 & 10 & 72 & 12 & 8 & 87.16 & 90.38 & 75.20 & 82.09 \\ \(\checkmark\) & 256 & 25 & 20 & 72 & 12 & 8 & 88.64 & 94.66 & 71.00 & 81.14 \\ \(\checkmark\) & 256 & 20 & 20 & 72 & 12 & 16 & 89.59 & 94.66 & 74.50 & 83.37 \\ \(\checkmark\) & 256 & 20 & 20 & 72 & 12 & 4 & 89.42 & 96.48 & 71.40 & 82.06 \\ - & 256 & 25 & 10 & 72 & 12 & 8 & 93.16 & 92.11 & 90.00 & 91.04 \\ - & 256 & 35 & 10 & 72 & 12 & 8 & 94.22 & 96.46 & 84.60 & 90.14 \\ - & 256 & 50 & 10 & 72 & 12 & 8 & 94.88 & 95.71 & 89.40 & 92.45 \\ - & 256 & 50 & 20 & 72 & 12 & 8 & 93.58 & 93.61 & 86.50 & 89.91 \\ - & 256 & 75 & 20 & 72 & 12 & 8 & 94.55 & 95.81 & 86.90 & 91.13 \\ - & 256 & 55 & 20 & 72 & 8 & 8 & 95.03 & 96.32 & 86.50 & 91.14 \\ - & 256 & 75 & 20 & 72 & 8 & 8 & 95.35 & 97.50 & 86.10 & 91.44 \\ - & 256 & 55 & 20 & 72 & 6 & 8 & 95.19 & 97.07 & 89.60 & 93.18 \\ - & 256 & 75 & 20 & 72 & 6 & 8 & 92.48 & 86.25 & 91.00 & 88.56 \\ - & 256 & 65 & 10 & 72 & 8 & 8 & 95.36 & 95.86 & 90.50 & 93.10 \\ - & 256 & 65 & 10 & 64 & 8 & 8 & 95.46 & 97.11 & 87.50 & 92.05 \\ - & 256 & 65 & 10 & 64 & 8 & 4 & 95.63 & 97.04 & 88.60 & 92.62 \\ - & 128 & 65 & 10 & 64 & 8 & 2 & 94.20 & 91.76 & 91.40 & 91.58 \\ - & 512 & 55 & 10 & 64 & 8 & 4 & 95.30 & 97.22 & 87.70 & 92.21 \\ - & 512 & 65 & 10 & 72 & 8 & 4 & 95.48 & 97.98 & 87.40 & 92.38 \\ - & 256 & 65 & 10 & 72 & 4 & 8 & 92.20 & 97.04 & 78.70 & 86.91 \\ - & 256 & 65 & 10 & 72 & 6 & 8 & 96.60 & 96.70 & 96.23 & 96.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results of hyperparameter tuning process. One of the most evident findings from the fine-tuning process was the significant impact of the training epoch count on total accuracy. When trained with a low number of epochs, the model's accuracy consistently ranged between 85% and 89%. Conversely, an increased number of epochs resulted in higher accuracy, although this came with a potential risk of overfitting. In the search for an appropriate number of epochs, a progressive approach was adopted, starting from lower values and gradually increasing, considering the dataset size. Subsequently, an early stopping mechanism, which stopped if there was no improvement after 10 epochs, was integrated into the training process using the estimated optimal parameters. The evaluation revealed that the training process typically converged around 65 epochs, with no significant improvement in accuracy observed beyond this point. Another important factor influencing performance was the learning rate scheduling strategy applied during training. To control the training dynamics, a learning rate scheduler was utilized, decreasing the learning rate by a scaling factor of 0.3 after three consecutive epochs, with the intention of refining the gradient descent process. However, this strategy unexpectedly resulted in a decrease in model performance. Consequently, the scheduler was excluded from the final training procedure, and the AdamW optimizer, recognized as a state-of-the-art optimization algorithm, was employed exclusively. The next aspects to be considered were the image resize and patch size parameters. Image resize defines the upscaled size of the original image, addressing the issue of lower image resolution. Patch size represents the size of the patches in pixels. A slight decrease in the image resize parameter results in a performance improvement. A similar effect is observed when slightly adjusting the patch size parameter within a small window.