## 4 Experimentation and Results
### Model Evaluation Metrics  
In this section, we will present each calculation table and results for each metric along with a graph for each table to illustrate the data. We studied the model's accuracy throughout the training, validation, and test sets. The accuracy is measured and established on the count of accurate detections related to the full number of images in the dataset. The accuracy percentages point out how well the model carried out on the respective set, with the validation batch demonstrating the highest accuracy at 85.00%, as explained in Table 5.  
As shown in Table 4, the validation set achieved the highest accuracy at 85.00%, which was slightly higher than the training set (84.22%) and the test set (84.67%). This suggests that the model is an overfit to the training set and has excellent generalizability. The model works well on unseen data, which is essential for its use in retail settings, as seen by the consistent performance across all sets. The model is flexible under diverse conditions, as seen by the slight variation in performance amid the training and test sets, which also points to minimal overfitting. Figure 10 explains a bar graph that compares the model's accuracy to the training, validation, and test datasets. For the validation set viewing the major measure, the bar constitutes the accuracy achieved on the dataset. The model's accuracy is compared throughout the datasets in Figure 10. The validation set presents the most accuracy, which is most likely because of training optimization. This cross-dataset consistency demonstrates that the model may function effectively in real-time applications, such as monitoring customer behavior, with minimal errors. Table 6 presents the precision aspect of the modeling crosswise training, validation, and test sets. Precision is determined by dividing the number of True Positives (TPs) by the total number of True Positives and False Positives (FPs). According to Table 6, the training set demonstrates the ultimate precision at 91.67%, exceeding the precision observed in as much of the validation and test sets. Figure 10: Graph of the model accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Total Images** & **Correct Detections** & **Model Accuracy (\%)** \\ \hline Training Set & 653 & 550 & 84.22\% \\ Validation Set & 200 & 170 & 85.00\% \\ Test Set & 150 & 127 & 84.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of the model accuracy. We illustrate the precision throughout the various datasets, demonstrating the model's capability to accurately identify true positives with minimal false positives, as shown in Figure 11.  
Table 7 shows the recall evaluation for the model throughout the training, validation, and test sets. Recall is the proportion of True Positives (TPs) to the total of True Positives and False Negatives (FNs). The recall values are slightly lower than precision with the validation set showing the highest recall at 85.00%. The recall performance of the model throughout the datasets demonstrates its efficiency in capturing true positives; there are still false negatives, as shown in Figure 12.  
In Equation (1), TP represents the positive class predicted as positive, FP the negative class predicted as positive, and FN the positive class predicted as negative. \[\text{F1}-\text{Score}=2*\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{1}\]  
The F1-score is a harmonic mean of precision and recall, providing a balanced measure of the model's performance. Table 8 shows the F1-scores for the training, validation, and test sets with the training set achieving the highest score at 87.79%. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **True Positives (TP)** & **False Positives (FP)** & **Precision** \\ \hline Training Set & 550 & 50 & 91.67\% \\ Validation Set & 170 & 20 & 89.47\% \\ Validation Set & 127 & 15 & 89.44\% \\ \hline \end{tabular}
\end{table} 
Table 6: Overview of precision. Figure 11: Graph of the precision through the datasets.