## 4 Discussion  
### Training Results  
This paper compares pre-trained CNN classifiers ResNet-18/50/101, ResNeXt-50/101 and EfficientNet-B3 for the CODEBRIM database, following full fine-tuning and using the same number of epochs (25), and the batch size was 32. All models were compiled with the loss function Focal Loss, a variant of BCEWithLogitsLoss, to improve the performance on imbalanced or hard-to-classify examples, where a model may predict dominant examples (classes with a lot of data) and ignore rarer examples. This approach is particularly effective in imbalanced datasets, such as defect classification tasks, and was originally proposed by [PERSON] et al. [42]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Train Dataset** &
\begin{tabular}{c} **Validation** \\ **Dataset** \\ \end{tabular} & **Test Dataset** & **Sum** \\ \hline Crack & 2208 & 149 & 150 & 2507 \\ \hline Spalling & 1608 & 140 & 150 & 1898 \\ \hline Efflorescence & 543 & 140 & 150 & 833 \\ \hline Exposed Bars & 1215 & 142 & 150 & 1507 \\ \hline Corrosion Stain & 1263 & 146 & 150 & 1559 \\ \hline Background & 2185 & 150 & 150 & 2485 \\ \hline Sum & 9022 & 867 & 900 & 10,789 \\ \hline \end{tabular}
\end{table} 
Table 4: Number of classes counts in this study. Figure 8: ResNet architecture for this study. The model leverages residual connections to improve feature learning and stability during training. Multi-label predictions are produced using sigmoid activation. The dataset was split into training, validation and test sets based on class-balanced partitioning. The class distribution across the subsets is presented in Table 4. For preprocessing, input images were resized to 224 \(\times\) 224 for ResNet and ResNeXt and 300 \(\times\) 300 for EfficientNet-B3 and normalized using ImageNet statistics. Data augmentation (random horizontal flip and rotation) was applied only to the training set. In this study, we applied standard augmentations, including horizontal flipping and random rotation, to increase the dataset diversity. These simple yet effective transformations were selected due to their relevance to typical variations in bridge inspection imagery. The same preprocessing pipeline was applied across all models to ensure comparability. The use of consistent data loaders and identical training parameters across all models ensures fair benchmarking and supports the reproducibility of the results. All parameters in the classifiers were optimized with the AdamW optimizer with an initial learning rate of 0.001. Figure 9a shows a steady improvement across all models over all epochs; the ResNet-18 model has the lowest accuracy (86.1%) at the beginning of training, while more sophisticated models such as ResNeXt-50 and EfficientNet-B3 start with accuracies of 89.71% and 89.02%. For EfficientNet-B3 and ResNeXt-101 at the end of the training, their accuracies reached 97.57% and 98.46%. They were consistently among the models with the highest accuracy. It shows these models have the ability to learn better and extract deeper features. ResNet-50 and ResNet-101 also performed well, achieving accuracies of 97.39% and 97.7%, but were still slightly lower than EfficientNet-B3 and ResNeXt-101. In contrast, ResNet-18 had the lowest accuracy at 95.55%, indicating the model's limitations in learning more complex features of the data. Figure 9b demonstrates that all the models successfully minimize errors as the training progresses. At the beginning of training, the ResNet-18 model has the highest error rate with a value of 0.0871, while the ResNet-50, ResNet-101, ResNeXt-50, ResNeXt-101 and EfficientNet-B3 models have lower error rates and perform better in optimization from the beginning. As the most optimal models, EfficientNet-B3 and ResNeXt-101 record the lowest error rate at the end of the training, which indicates their high ability to learn complex data features. In contrast, ResNet-18 has a slower error rate, and its loss rate remains at 0.0303 at the end of training, which is still higher than the other models.