## 4 Results and Discussion
### Evaluation of Model Quantization  
The evaluation metrics for the results of the quantization process of the digit detection model are presented in Table 6. Based on the results of 16-bit quantization, there were no significant changes in accuracy, precision, recall, F1-score, and model size. In fact, the changes in these five metrics were almost negligible, approaching 0%. However, there was a significant reduction in inference time, particularly for the small variant, which exhibited a decrease of 36.60%. Model size remained relatively unchanged because, as stated before, the base model is a PyTorch model, so they are already stored in 16-bit floating-point precision. Overall, the FP16 version of the YOLOv8-small variant proved to have the smallest size and the fastest inference speed. In contrast, with 8-bit quantization, there was a significant reduction in both model size and inference time. Model size was successfully compressed to half that of the base model. The INT8 version of the YOLOv8-small variant had the smallest size, at only 11 MB. Generally, there was a slight decrease in precision for models quantized to INT8 precision, ranging from 2.50% to 4.00%. The YOLOv8-medium (YOLOv8m) model was the best-performing INT8 model in terms of accuracy, precision, and F1-score, although compared to other INT8 models, the differences were insignificant. So, based on the results, it can be concluded that quantization was effective in compressing the model. This is demonstrated by the fact that quantization generally reduces model size and inference time without significant drops in accuracy, precision, recall, and F1-score. When considering all quantized variants, the FP16 version of the YOLOv8-medium (YOLOv8m) model achieved the best accuracy, precision, recall, and F1-score. However, the differences in these four metrics were not significant compared to the other quantized models. Since the model is intended for deployment in a mobile (mHealth) application, model size and inference time are the primary considerations in selecting the final model. Based on the data, the INT8 version of the YOLOv8-small (YOLOv8s) variant had the smallest size compared to the other quantized models. Additionally, its inference time was not significantly different from that of the FP16 version of the YOLOv8-small variant, which had the fastest inference time. To formalize the selection of the final model, we conducted a Pareto analysis incorporating four criteria: accuracy, F1-score, inference time, and model size. In this analysis, predictive performance is represented as the mean of the accuracy and F1-score, while computational cost is defined as the mean of the normalized inference time and model size. These metrics were plotted in two-dimensional space, with performance on one axis and computational cost on the other. Prior to visualization, inference time and model size were each normalized to the range [0, 1], where 0 corresponds to the minimum and 1 to the maximum observed value across all models. Models located on the Pareto frontier are considered optimal, as they achieve a favorable trade-off--maximizing predictive performance while minimizing computational demands. As shown in Figure 8, YOLOv8-small INT8, YOLOv8-small FP16, and YOLOv8-medium FP16 lie on the Pareto frontier, representing models that offer the best trade-off between predictive performance and computational efficiency. This analysis highlights  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**YOLOv8**} & \multicolumn{4}{c}{**YOLOv8-**} & \multicolumn{4}{c}{**YOLOv8**} \\ \cline{2-13}  & **Base** & **FP16** & **A(\%)** & **DFN** & **A(\%)** & **DFN8** & **A(\%)** & **DFN8** & **A(\%)** & **DFN8** & **A(\%)** & **DFN8** & **A(\%)** \\ \hline Accuracy (\%) & 99.70 & 99.70 & 0.00 &  
their potential for deployment in resource-constrained mHealth applications, where both accuracy and runtime efficiency are critical.