## 1 Introduction  
Nowadays, transformers have become the preferred models for performing Natural Language Processing (NLP) tasks. They offer scalability and computational efficiency, allowing models to be trained with more than a hundred billion parameters without saturating model performance. Inspired by the success of the transformers applied to NLP and assuming that the self-attention mechanism could also be beneficial for image classification tasks, it was proposed to use the same architecture, with few modifications, to perform image classification [1]. The author's proposal was an architecture, called Vision Transformers (ViT), which consists of breaking the image into 2D patches and providing this linear sequence of patches as input to the model. Figure 1 presents the architecture proposed by the authors. In contrast to this deep learning architecture, there is another very popular tool for processing large volumes of data called Convolutional Neural Networks (CNN). The CNN is an architecture that consists of multiple layers and has demonstrated good performance in various computer vision tasks such as object detection or image segmentation, as well as NLP problems [2]. The typical CNN architecture starts with convolutional layers that pass through the kernels or filters, from left to right of the image, extracting computationally interpretable features. The first layer extracts low-level features (e.g., colours, gradient orientation, edges, etc. ), and subsequent layers extract high-level features. Next, the pooling layers reduce the information extracted by the convolutional layers, preserving the most important features. Finally, the fully-connected layers are fed with the flattened output of the convolutional and pooling layers and perform the classification. Its architecture is shown in Figure 2.  
With the increasing interest in Vision Transformers as a novel architecture for image recognition tasks, and the established success of CNNs in image classification, this work aims to review the state of the art in comparing Vision Transformers (ViT) and Convolutional Neural Networks (CNN) for image classification. Transformers offer advantages such as the ability to model long-range dependencies, adapt to different input sizes, and the potential for parallel processing, making them suitable for image tasks. However, Vision Transformers also face challenges such as computational complexity, model size, scalability and scalability. Figure 1: Example of an architecture of the ViT, based on [1]. Figure 2: Example of an architecture of a CNN, based on [2]. to large datasets, interpretability, robustness to adversarial attacks, and generalization performance. These points highlight the importance of comparing ViTs with older and established CNN models. The overall goal of this work is to understand what conditions have the most influence on the performance of the two Deep Learning architectures, and what characteristics differ between the two architectures, that allow them to perform differently for the same objective. Some of the aspects that will be compared include datasets considerations, robustness, performance, evaluation, interpretability, and architecture. Specifically, we aim to answer the following research questions:  
RQ1--Can the ViT architecture have a better performance than the CNN architecture, regardless of the characteristics of the dataset? RQ2--What influences CNNs that do not to perform as well as ViTs? RQ3--How does the Multi-Head Attention mechanism, which is a key component of ViTs, influence the performance of these models in image classification? In order to address these research questions, a literature review was conducted by searching various databases such as Google Scholar, Scopus, Web of Science, ACM Digital Library, and Science Direct using specific search terms. This paper presents the results of this review and analyses the methodologies and findings from the selected papers. The rest of this paper is structured as follows.