## 3 Results and Discussion of CNN Models Classification
### Results for Resnet101 Model  
The evaluation results for the Resnet101 model are presented in Table 8. The model's accuracy varied across the folds, with the highest accuracy of 66.42% observed in the fifth fold and the lowest accuracy of 56.04% observed in the second fold. The overall accuracy across all folds was 61.19%. The evaluation metrics for the ResNet101 model, as presented in Table 9, provide a comprehensive analysis of the model's performance across the \"Green\", \"Yellow\", and \"Red\" classes. These metrics include precision, sensitivity (recall), specificity, accuracy, and F1 score, offering a detailed view of how well the model performs for each class. For the \"Green\" class, the model achieves a precision of 81.45% and a sensitivity of 80.48%. The F1 score of 80.96% indicates a balanced performance in identifying \"Green\" instances, with relatively few misclassifications. The model's specificity of 85.40% and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fold / Drill \#**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Critical Errors**} \\ \cline{3-5}  & & **Green as Red** & **Red as Green** & **Total** \\ \hline
1 & 66.26\% & 26 & 13 & 39 \\
2 & 56.04\% & 158 & 7 & 165 \\
3 & 65.59\% & 5 & 39 & 44 \\
4 & 56.13\% & 64 & 2 & 66 \\
5 & 66.42\% & 3 & 16 & 19 \\ Total & 61.19\% & 256 & 77 & 333 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results of Resnet101 model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline Green & 81.45 & 80.48 & 85.40 & 83.22 & 80.96 \\ Yellow & 47.52 & 36.96 & 80.04 & 65.89 & 41.58 \\ Red & 46.23 & 62.08 & 78.65 & 74.87 & 52.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Detailed evaluation metrics for ResNet101 model. overall accuracy of 83.22% suggest that it is generally reliable in distinguishing the \"Green\" class from others. The \"Yellow\" class poses a significant challenge for the model, as reflected by the lower precision of 47.52% and sensitivity of 36.96%. The F1 score of 41.58% highlights the model's struggle to accurately classify \"Yellow\" instances, often leading to confusion with the \"Green\" and \"Red\" classes. Although the specificity for the \"Yellow\" class is reasonably high at 80.04%, the overall accuracy is lower at 65.89%, indicating that a considerable number of \"Yellow\" instances are misclassified. For the \"Red\" class, the model shows moderate performance with a precision of 46.23% and a sensitivity of 62.08%. The F1 score of 52.99% reflects the model's tendency to misclassify \"Red\" instances, although it performs slightly better than for the \"Yellow\" class. The specificity of 78.65% and accuracy of 74.87% suggest that the model can somewhat differentiate \"Red\" instances, but still faces challenges in correctly classifying all of them. In summary, while the ResNet101 model demonstrates reasonably strong performance for the \"Green\" class, its effectiveness decreases significantly for the \"Yellow\" and \"Red\" classes. The lower precision and F1 scores for these classes indicate a need for further refinement and optimization of the model, particularly in addressing the classification challenges associated with intermediate and heavily worn drill conditions. The confusion matrix for the Resnet101 model, as shown in Figure 10, provides a detailed breakdown of the classification performance across all folds. The matrix indicates the number of correctly and incorrectly classified instances for each class. In the confusion matrix, the true positive rates for the classes \"Green\", \"Yellow\", and \"Red\" are as follows:  
* The \"Green\" class had 2780 correctly classified instances, with 846 instances misclassified as \"Yellow\" and 256 instances as \"Red\". * The \"Yellow\" class had 1394 correctly classified instances, with 623 instances misclassified as \"Green\" and 938 instances as \"Red\".