## 3 Results and Discussion of CNN Models Classification
### Results for Resnet101 Model
* The \"Red\" class had 962 correctly classified instances, with 906 instances misclassified as \"Yellow\" and 77 instances as \"Green\". These results indicate that the Resnet101 model achieved moderate accuracy, with varying degrees of misclassification among the classes. The model showed a higher tendency to misclassify instances of the \"Yellow\" class, particularly as \"Green\" and \"Red\". Figure 10: Overall confusion matrix for all folds for Resnet101 model. Critical errors refer to the misclassification of instances where the model incorrectly identifies a \"Green\" drill hole as \"Red\" or vice versa. Such errors are particularly concerning due to their potential implications in real-world applications. * Fold 1--the model demonstrated a relatively high accuracy of 66.26%. Critical errors included 26 instances where a \"Green\" hole was classified as \"Red\", and 13 instances where \"Red\" holes were classified as \"Green\". The total number of critical errors in this fold was 39. * Fold 2--the accuracy for this fold was 56.04%. Critical errors increased significantly, with 158 instances of \"Green\" holes misclassified as \"Red\", and seven instances of \"Red\" holes misclassified as \"Green\". The total number of critical errors in this fold was 165. * Fold 3--this fold showed an accuracy of 65.59%. There were five instances where a \"Green\" hole was misclassified as \"Red\", and 39 instances where \"Red\" holes were misclassified as \"Green\". The total number of critical errors was 44. * Fold 4--the accuracy dropped to 56.13% in this fold. A significant number of 64 instances where \"Green\" holes were classified as \"Red\" were observed. Only two instances of \"Red\" holes misclassified as \"Green\" were recorded. The total number of critical errors was 66. * Fold 5--the accuracy for this fold was 66.42%. Critical errors included three instances where a \"Green\" hole was classified as \"Red\", and 16 instances where \"Red\" holes were classified as \"Green\". The total number of critical errors in this fold was 19. The analysis of critical errors reveals several insights into the performance of the Resnet101 model:  
1. Consistency in Green to Red misclassification--across all folds, there is a consistent occurrence of \"Green\" holes being misclassified as \"Red\". This misclassification is particularly problematic as it could lead to unnecessary corrective actions or rework. 2. Variation in Red to Green misclassification--the misclassification of \"Red\" holes as \"Green\" varies significantly across folds. For instance, Fold 4 had only two such misclassifications, while Fold 3 had a higher number (39 instances). 3. Impact on overall accuracy--the high number of critical errors, especially in certain folds, significantly impacts the overall accuracy of the model. Folds with higher critical errors generally correspond to lower accuracy rates. 4. Need for model improvement--the analysis suggests that while the Resnet101 model shows promise, there is a need for further refinement to reduce the critical error rate. This could involve tuning the model parameters, using more advanced architectures, or incorporating additional data preprocessing techniques. By addressing these critical errors, the reliability and applicability of the Resnet101 model for classifying drilled holes in melamine-faced chipboard can be enhanced, leading to more consistent and accurate results in practical applications.