## 2 Materials and Methods
### Fine-Tuning and Transfer Learning Strategies  
The chosen models, ResNet, ResNeXt and EfficientNet, are three diverse architectural design philosophies in deep convolutional neural networks. ResNet models are essentially residual connections to help with the learning of deep features. ResNeXt, with the introduction of grouped convolutions, allows the model to process on multiple parallel paths of information that may improve the detection of fine-grained features or overlapping defect patterns. EfficientNet-B3 employs a compound scaling approach that uniformly scales the depth, width and resolution of the network, allowing it to better capture fine texture variations and small-scale surface defect features. Real-life situations often come with imperfect and limited data with complex information and related features of interest. Transfer learning and fine-tuning are critically important for improving the efficiency and speed of CNN training; typically, pre-trained models are loaded on large datasets (e.g., ImageNet), and the final layers of the model are then modified accordingly. Figure 5: Schematic representation of EfficientNet-B3 [36]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Stage** & **Output** & **ResNet-18** & **ResNet-50** & **ResNet-101** & **ResNeXt-50** & **ResNeXt-101** \\ \hline conv1 & \(112\times 112\) & \(7\times 7\), 64, stride 2 & \(7\times 7\), 64, stride 2 & \(7\times 7\), 64, stride 2 & \(7\times 7\), 64, stride 2 & \(7\times 7\), 64, stride 2 \\ \hline conv2 & \(56\times 56\) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) \\ \hline \multirow{3}{*}{conv2} & \multirow{3}{*}{\(56\times 56\)} & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) & \( \begin{array}{c}3\times 3\text{ max pool,}\\ \text{stride 2}\end{array} \) \\ \cline{2-6}  & & \([3\times 3,64]\times 2\) & \(\begin{array}{c}1\times 1,64\\ \begin{array}{c}3\times 3\text{,64}\\ \begin{array}{c}1\times 1,256\end{array}\end{array} \) & \(\begin{array}{c}1\times 1,64\\ \begin{array}{c}3\times 3\text{,64}\\ \begin{array}{c}1\times 1,256\end{array}\end{array} \) & \( \begin{array}{c}1\times 1,128\\ \begin{array}{c}1\times 1,256\end{array}\end{array} \) & \(\begin{array}{c}3\times 3\text{,C}\\ \begin{array}{First, the weights of all the convolutional layers of the ResNet, ResNeXt and EfficientNet models are initialized using pre-trained weights from the ImageNet dataset [13]. These pre-trained convolutional layers act as powerful feature extractors for images [37]. In this study, we apply a full fine-tuning approach, meaning that all layers of the network, including the convolutional ones, are updated during training. This differs from the standard transfer learning strategy, which typically involves freezing the convolutional base and re-training only the fully connected (FC) layers [38; 39]. Fine-tuning all the layers allows the model to better adapt to the domain-specific characteristics of concrete defect images [27]. Figure 6 illustrates the key differences between conventional transfer learning and the full fine-tuning approach employed in this study. While transfer learning typically updates only the final classification layers, full fine-tuning involves training the entire network, including all convolutional and fully connected layers. This strategy enables the model to adapt more effectively to the specific patterns and features present in the target domain.