## 3 Results
### Evaluating the Efficiency of the YOLO v10 XE Model in Analyzing Building Roof Topologies
#### 3.2.2 Training the YOLO v10 XE Model  
The model was trained using the Adam optimizer, which is commonly used for its efficiency in convergence to minimize the loss function with large datasets and adaptive learning rate adaptation [44; 45]. The training parameters, such as the batch size (set at 128) and the number of epochs, were selected based on experimental validation and learning curve analysis. The batch size had an effect on gradient stability and learning efficiency; the literature suggests that smaller batch sizes may lead to better generalization [46]. The number of epochs was increased to 350 with an early stop option (detected on the 327 th epoch (Figure 7) in order to achieve full convergence of the model without the risk of overfitting. This was confirmed by the validation metrics, loss function analysis, and mAP (mean Average Precision). It is crucial to highlight that the model was developed through transfer learning, utilizing a pre-trained MS COCO (Microsoft Common Objects in Context) dataset. This approach involved freezing the weights of the backbone layers responsible for extracting general visual features, such as points, edges, and textures.  
The evaluation of model performance was based on a number of metrics, including precision, recall, the F1-score measure and accuracy. These metrics are standard in the evaluation of classification models and facilitate a comprehensive analysis of model performance [47; 48; 49]. A detailed description of the indicators used is included in Appendix A.1.  
Figure 7: Training and validation loss and precision trends across 350 epochs. Early stopping at epoch 327 ensures optimal precision and prevents overfitting (source: author’s own elaboration).  
Using training data for only two classes in the YOLO v10 XE model and limiting the area to one region results in a simplified and unbalanced set. During data preparation, it was found that one class (gabled roofs) had a significant advantage over the others. It was therefore decided to merge the other classes into one (other roofs). This may result in an imperfect classification for this second class. The YOLO model may focus on the dominant class and ignore the less common roof types, resulting in low classification accuracy. The presence of irregular roofs constituted an additional challenge. The presence of objects of this nature resulted in a generation of false positives and false negatives during the classification process. The background complexity of the image data also proved to be a significant difficulty. The variety of backgrounds (e.g., the presence of dense vegetation, shadows, vehicles, and other infrastructure elements) affected the stability of the model during the optimization process. The model encountered difficulties in distinguishing target objects from background noise. It was also evident that there was an issue with the convergence of the model during the training process, particularly in the initial epochs. The extensive number of parameters in YOLO v10 XE necessitated meticulous calibration of the hyperparameters to circumvent substantial fluctuations in the loss function values. Consequently, the training process was susceptible to the phenomenon of overfitting to more prevalent examples in the data, while disregarding more challenging instances.