###### Abstract
Thus, having a high-quality, well-curated dataset of UML diagrams can offer numerous benefits for the industry in practical areas, such as software reuse [13], code generation [14], and design validation [15]. Additionally, it could be highly valuable for teaching and research. Although a few UML diagram datasets exist in the literature [16; 17; 18], it is common to find duplicate elements in the existing datasets, which may affect the evaluation of the models obtained when using these datasets. Therefore, further progress is needed in the construction of high-quality and reliable datasets. To automate the construction of UML diagram datasets, since these diagrams are commonly shared as images, accurate classification of UML images is crucial. Early efforts focused on classifying UML diagrams by applying machine learning. For example, in [19] the authors applied machine learning techniques to automatically classify images based on whether they contained a UML diagram or not. However, the superior capability of deep learning techniques for image classification compared to traditional machine learning [20] made these techniques the ideal tool for UML diagrams classification and a useful approach to advance the construction of UML diagram datasets. Therefore, shortly after the appearance of machine learning-based works such as [19], several studies emerged within the context of deep learning to address the automatic classification of UML diagrams into multiple classes. For example, the feasibility of using deep learning techniques to classify four different types of UML diagrams employing various convolutional neural network (CNN) architectures was demonstrated in [16], yielding outstanding results. Following this success, that work was expanded to include 10 different types of UML diagrams in [17]. Different works also recognized the importance of developing an automatic classification tool for UML diagrams and explored different approaches using transfer learning with various neural network architectures [18; 21]. Collectively, they emphasized the importance of creating a comprehensive image dataset to facilitate and support further studies. In this paper, we take a step towards creating a high-quality ground truth dataset of UML diagrams, which includes semi-automated inspection supported by a software tool to remove duplicates and ensure the correct labeling of all UML diagrams contained in the dataset. Although the application of fully automated methods for dataset creation is more efficient, we believe in the critical role of human supervision to ensure the accuracy and reliability of the datasets created. Our curated dataset is a valuable and unique resource for the research community, as it serves as a foundation for training and evaluating diverse artificial intelligence models. In this paper, we demonstrate this by training and testing several deep learning models--three CNNs and three vision transformers (ViTs)--using our dataset, achieving highly satisfactory results compared to those presented in other works in the literature. In this regard, our experimental results underscore the potential of ViTs for UML diagram classification. To the best of our knowledge, despite the widespread application of ViTs in image classification tasks [22; 23; 24], they have not yet been applied to UML diagram classification. This sets our work apart from others that predominantly utilize CNNs for UML diagram classification.