## 5 Results
### Performances in Terms of Average Indices  
For the CNN classifiers, the results in terms of average indices are reported in Table 3.  
It is possible to see that most of the CNN classifiers had average f1-score indices close to 0.9, and therefore, were able to solve the problem in a satisfactory way, with a slight advantage for GoogLeNet (0.92) and AlexNet (0.91). If we consider the average time required for training, the ranking would be DarkNet (4.18 min), ShuffleNet (4.49 min), and AlexNet (7.29 min).  
Referring to the Transformer classifiers, Table 4 shows that most of them had average f1-score indices close to 0.9, indicating their ability to solve the problem effectively. Notably, the CvT and Twins-SVT both achieved an f1-score of 0.91, followed closely by the SwinT, TNT, and DeiT, with scores of 0.90. If we consider the average time required for training, the LeViT was the fastest (10.77 min), followed by the SwinT (39.85 min) and PvT (35.70 min). However, the CvT required the longest training time (117.19 min).  
Let us now consider in more detail the behavior of the classifiers with respect to the individual classes. For simplicity, we show the f1-score indices for the CNN classifiers and for the Vision Transformer classifiers.  
The f1-scores for CNN-based classifiers across different classes and architectures are presented in Table 5.  
The f1-scores for the Transformer-based classifiers across the same classes are presented in Table 6.  
The results indicate that both the CNN and Transformer-based models were effective for the classification of volcanic activity. However, the following should be noted:  
* The Transformer-based models showed slightly better performance for challenging classes, like 'Cooling or Lavaflow' and 'Degassing', likely due to their global attention mechanism.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric** & **ViT** & **SwinT** & **DeiT** & **PvT** & **CvT** & **TNT** & **LeViT** & **Twins-SVT** \\ \hline Precision & 0.88 & 0.91 & 0.90 & 0.90 & 0.91 & 0.91 & 0.84 & 0.92 \\ \hline Recall & 0.86 & 0.90 & 0.90 & 0.88 & 0.91 & 0.90 & 0.83 & 0.91 \\ \hline f1-score & 0.86 & 0.90 & 0.90 & 0.88 & 0.91 & 0.90 & 0.83 & 0.91 \\ \hline Training Time (min) & 78.90 & 39.85 & 70.98 & 35.70 & 117.19 & 48.55 & 10.77 & 58.30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary table of average Precision, Recall, f1-score, and Training Time (in minutes) for Transformer classifiers.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Metric** & **Alex** & **Dense** & **Dark** & **GoogLe** & **Res** & **Squeeze** & **Shuffle** & **VGG16** \\ \hline Precision & 0.92 & 0.88 & 0.89 & 0.92 & 0.89 & 0.90 & 0.89 & 0.89 \\ \hline Recall & 0.91 & 0.84 & 0.88 & 0.92 & 0.89 & 0.89 & 0.89 & 0.88 \\ \hline f1-score & 0.91 & 0.84 & 0.88 & 0.92 & 0.89 & 0.89 & 0.89 & 0.88 \\ \hline Training Time (min) & 7.29 & 48.81 & 4.18 & 13.27 & 30.70 & 9.72 & 4.49 & 62.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary table of average Precision, Recall, f1-score, and Training Time (in minutes) for CNN classifiers.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Class** & **Alex** & **Dense** & **Dark** & **GoogLe** & **Res** & **Squeeze** & **Shuffle** & **VGG16** \\ \hline Cloudy & 0.93 & 0.97 & 0.91 & 0.94 & 0.87 & 0.88 & 0.91 & 0.90 \\ \hline Cooling or lavaflow & 0.91 & 0.76 & 0.88 & 0.90 & 0.89 & 0.73 & 0.92 & 0.92 \\ \hline Degassing & 0.90 & 0.93 & 0.88 & 0.90 & 0.87 & 0.82 & 0.89 & 0.89 \\ \hline Lava fountain & 0.92 & 0.98 & 0.90 & 0.95 & 0.89 & 0.88 & 0.89 & 0.88 \\ \hline No activity & 0.89 & 0.97 & 0.86 & 0.94 & 0.86 & 0.82 & 0.89 & 0.89 \\ \hline Strombolian & 0.89 & 0.95 & 0.84 & 0.93 & 0.88 & 0.85 & 0.89 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: f1-score for each class using CNN classifiers.