## 2 Materials and Methods
### CNN Models Architecture  
In our research, we assessed the capabilities of three distinct Convolutional Neural Network (CNN) models--VGG16, VGG19, and ResNet101--for identifying and classifying drilled holes in melamine-faced chipboards, as referenced in various works [40; 45; 58; 59; 60]. These models were selected because of their proven proficiency in a variety of image analysis tasks. Herein, we detail each model's architecture and the specific setups utilized in our study. Introduced by [PERSON] and [PERSON] [61], the VGG16 architecture is favored for its straightforward design and depth, featuring 16 weight layers. This configuration comprises 13 convolutional layers paired with three fully connected layers at the end. It applies \(3\times 3\) convolutional filters through its layers, enabling it to discern intricate patterns in the visuals. Max-pooling layers are strategically placed to decrease the spatial dimensionality and computational demands. Expanding upon VGG16, the VGG19 model [61] incorporates an additional three convolutional layers, totaling 19 weight layers. This enhancement is aimed at augmenting the model's ability to learn more elaborate features, albeit at the expense of increased computational requirements and extended training periods. The ResNet101 model, brought forth by [PERSON] et al. [62], belongs to the Residual Networks lineage. It introduces an approach of using residual learning to facilitate the training of profoundly deep networks. With its 101 layers, ResNet101 surpasses both VGG16 and VGG19 in depth. Its residual blocks are designed to learn identity functions, aiding in overcoming the issue of vanishing gradients and permitting the cultivation of deeper networks. Our investigation into the efficacy of various CNN architectures for the categorization of drilled holes in melamine-faced chipboards involved an analysis of three renowned pretrained models: VGG16, VGG19, and ResNet101. We took into account each architecture's depth, scale, parameter count, and optimal input image dimensions to gauge their effectiveness and efficiency for our specific task. These models were initially trained on the extensive ImageNet dataset, which encompasses a wide assortment of image categories. We then fine-tuned them on our targeted dataset of drilled holes in melamine-faced chipboards. The images were resized to dimensions of \(224\times 224\) pixels to conform to each model's input requirements. To enhance the training dataset's variety and fortify the models' resilience, data augmentation techniques like rotation, flipping, and scaling were applied. Table 2 presents a synopsis of the salient features of the pretrained CNN models explored in this study [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77]. The comparison of these pretrained CNN models is based on several criteria, including depth, model size, number of parameters, and input image size. Each of these factors plays a crucial role in determining the model's performance, computational requirements, and ability to generalize to the classification task at hand. * Depth--ResNet101, with its 101 layers, is the deepest model among the three. The depth of a model often correlates with its capacity to learn complex features. However, deeper models also require more computational resources and can be more challenging to train effectively. * Size--VGG16 and VGG19 are significantly larger in terms of model size compared to ResNet101. Larger models can store more information, but they also require more memory and storage, which can be a limitation in resource-constrained environments. * Parameters--VGG16 and VGG19 have a much higher number of parameters (138 million and 144 million, respectively) compared to ResNet101 (44.6 million). While having more parameters can increase a model's learning capacity, it also makes the model more prone to overfitting and increases the computational load. * Image input size--all models were adapted to accept input images of \(224\times 224\) pixels.