## 4 Evaluation and Results
### Comparison with CNN and YOLOv8 Baselines  
To comprehensively evaluate YOLOMal-XAI's performance, we benchmarked the YOLO11n variant against three widely adopted CNN architectures: ResNet-50 (deep and heavyweight), GoogLeNet (mid-sized with Inception modules), and MobileNetV2 (lightweight and mobile-optimized). All models were trained under identical conditions for 100 epochs using the same hyperparameters and datasets.  
Table 10 summarizes the binary and multi-class classification results. YOLO11n consistently outperforms all CNN baselines in both accuracy and training efficiency. It achieves 99.68% accuracy in binary classification with a training time of only 0.81 h, and 99.56% accuracy, 99.51% precision, 99.58% recall, and 99.55% F1-score in multi-class classification within 1.07 h. In contrast, ResNet-50, GoogLeNet, and MobileNetV2 exhibit lower accuracy and require significantly more computational time.  
These findings are further illustrated in Figure 7, which shows the learning curves of YOLO11n compared to CNN models. YOLO11n converges faster and more stably across both tasks. With only 1.6M parameters [35], it surpasses MobileNetV2 in both performance and efficiency, while also being more computationally practical than larger models like ResNet-50.  
In addition to CNN-based baselines, we benchmarked YoloMal-XAI against YOLOv8, a state-of-the-art architecture widely used for image classification. To ensure fairness, we selected comparable configurations from each family: YOLO11x and YOLOv8x for binary classification, and YOLO11n and YOLOv8n for multi-class classification. All models were trained using the same datasets, preprocessing pipeline, and hyperparameters.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Binary Classification**} & \multicolumn{6}{c}{**Multi-Class Classification**} \\ \cline{2-11}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Time (h)** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Time (h)** \\ \cline{2-11}  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Time (h)** \\ \hline ResNet50 & 99.40 & 99.45 & 99.74 & 99.60 & 5.94 & 97.88 & 97.92 & 97.88 & 97.88 & 5.97 \\ GoogLeNet & 99.27 & 99.57 & 99.45 & 99.51 & 3.36 & 97.79 & 97.85 & 97.93 & 97.80 & 3.42 \\ MobileNet & 99.24 & 99.28 & 99.70 & 99.49 & 8.38 & 97.66 & 97.71 & 97.66 & 97.66 & 8.34 \\
**YOLO11n** & **99.68** & **99.87** & **99.70** & **99.79** & **0.81** & **99.56** & **99.51** & **99.58** & **99.55** & **1.07** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of YOLO11n and CNN models on binary and multi-class classification tasks.  
Figure 7: Time vs. epoch plots comparing YOLO11n with CNN models for **(a)** binary and **(b)** multi-class classification tasks.  
As shown in Table 11, YOLO11x achieves a F1-score of 99.92% with a training time of only 6.42 h--significantly faster than YOLOv8x, which required 22.02 h to reach a comparable F1-score of 99.94%. This makes YOLO11x approximately 3.5\(\times\) more time-efficient.  
In the multi-class task, YOLO11n attained 99.56% accuracy and F1-score in just 1.07 h, outperforming YOLOv8n, which reached 98.87% accuracy and 98.86% F1-score in 0.99 h. Despite YOLOv8n's slightly faster runtime, YOLO11n achieved superior classification metrics.