## 1 Introduction  
The recent advancements in Natural Language Processing (NLP) owe much to the introduction of the Transformer architecture by [PERSON] et al. [1], which revolutionized text analysis by leveraging attention mechanisms to extract meaningful information. This success has inspired the adaptation of Transformers to other domains, including computer vision. A pivotal development in this transition was the Vision Transformer (ViT) proposed by [PERSON] et al. [2], which demonstrated that images could be processed similarly to text by segmenting them into non-overlapping patches and applying self-attention mechanisms. This innovation enabled Transformers, previously confined to text-based applications, to excel at image classification tasks, rivaling the long-standing dominance of Convolutional Neural Networks (CNNs). CNNs have traditionally been the cornerstone of image classification due to their ability to capture spatial hierarchies through convolutional operations. Their hierarchical learning of features has made them a benchmark for many computer vision tasks [3, 4]. However, Vision Transformers present a compelling alternative by capturing long-rangedependencies in images without relying heavily on the inductive biases inherent to convolutional operations. This fundamental difference has sparked a competitive dynamic between the two architectures, each offering unique advantages and facing distinct challenges. Building on previous research [5; 6], where CNNs were employed to classify thermal images of volcanic activity captured by ground-based thermal cameras, this study explored whether Vision Transformers could provide additional benefits. Prior work [7] demonstrated the effectiveness of various CNN architectures for analyzing volcanic images, but the potential of Vision Transformers to enhance the classification performance in this context remains unexamined. Given the complexity of thermal volcanic images, where both local and global feature dependencies are crucial, evaluating Transformers against CNNs is particularly pertinent. The hypothesis of this study was that Vision Transformer models can outperform traditional CNNs in the classification of thermal images of volcanic activity, particularly in scenarios requiring the handling of complex and ambiguous classes. The research questions addressed in this study were as follows:  
* Can Vision Transformer models achieve higher classification accuracies compared with CNNs for thermal images of volcanic activity? * How do Vision Transformer models perform in terms of efficiency and inference time compared with CNNs? * What are the specific strengths and weaknesses of Vision Transformer models versus CNNs in this application? The objectives of this study were as follows:  
* To compare the classification performance of Vision Transformer models and CNNs on a dataset of thermal images of Mount Etna's volcanic activity. * To evaluate the efficiency and inference time of both model types. * To identify the scenarios where Vision Transformer models provide significant benefits over CNNs. The motivation behind this study was to improve the accuracy and reliability of volcanic-activity-monitoring systems. The accurate classification of thermal images is crucial for early warning systems and scientific research on volcanic phenomena. By exploring the potential of Vision Transformer models, we aimed to advance the state of the art in this field. The novelty of this study lay in the extensive comparison between the most representative Convolutional Neural Networks (CNNs) and the latest Transformer networks for this specific application. Unlike previous studies that primarily focused on CNN-based techniques, our work provided new insights into the strengths and weaknesses of each approach. Our findings contribute to the broader field of computer vision by demonstrating the potential of Transformer networks in specialized applications, such as volcanic activity monitoring, where accurate and reliable image classification is crucial.