wseas-12080-20260923082850-c5efb7 20260923082850911 wseas/wseas wseas.group@gmail.com WSEAS WSEAS Transactions on Computers 1109-2750 2224-2872 04 14 2026 04 14 2026 25 M. Sabrigiriraj Department of It Hindusthan College of Engineering and Technology Coimbatore INDIA K. Manoharan Department of Ece Sns College of Technology Coimbatore INDIA Vision Transformers are normally used in image recognition and are considered as an alternative to Convolutional Neural Networks. Unlike traditional convolution-based models, a Vision Transformer does not rely on local filters Instead, it segments an image into smaller patches and understands the relationships among individual patches. This technique is powerful, but it can be challenging for beginners to understand when described only with equations or programming concepts. Hence, this paper showcases the process of Vision Transformers using simple examples and visual illustrations. This paper is intended for students, teachers, and beginners who are interested in learning how transformer models are used in computer vision. The tutorial explains how an image is divided into patches, how each patch is transformed into a token, how self-attention helps different image regions share information, and how the transformer encoder helps the final classification process. The foremost purpose of this tutorial is to support readers in developing a clear understanding of Vision Transformers. After gaining this intuitive understanding, readers can more easily study the mathematical particulars and implementation techniques later. 09 23 2026 09 23 2026 160 15 https://creativecommons.org/licenses/by/4.0/ 10.37394/23205.2026.25.15 https://wseas.com/journals/articles.php?id=12080 M. Sabrigiriraj, K. Manoharan. Teaching Machine Learning and Deep Learning Introduction: An Innovative Tutorial-Based Practical Approach. WSEAS Transactions on Advances in Engineering Education. Vol. 21, pp. 54–61, 2024.10.37394/232010.2024.21.8 S. Khan, M. Naseer, M. Hayat, S. Waqas Zamir, F. S. Khan and M. Shah "Transformers in vision: A survey." ACM computing surveys (CSUR) Vol. 54, no. 10s, pp. 1–41, 2022. K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, E. Xu, C. Xu, Y. Xu and D. Tao "A survey on vision transformer." IEEE transactions on pattern analysis and machine intelligence Vol. 45, no. 1, pp. 87–110, 2023. Y. Liu, T. Zhang, K. Chen, X. Liu, Y. Deng and F. Wang, "A survey of visual transformers." IEEE transactions on neural networks and learning systems Vol. 35, no. 6, pp. 7478–7498, 2024. Jamil, Sonain, Md Jalil Piran, and Oh-Jin Kwon. "A comprehensive survey of transformers for computer vision." drones, Vol. 7, no. 5, Art. no. 287, 2023. A. Khan, M. Khan, S. Khan and M. A. Khan "A survey of the self-supervised learning mechanisms for vision transformers." arXiv preprint arXiv:2408. 17059 (2024). Fournier, Quentin, Gaétan Marceau Caron, and Daniel Aloise. "A practical survey on faster and lighter transformers." ACM Computing Surveys Vol. 55, no. 14s, pp. 1–40, 2023. R. Kashefi, M. Shafiee, A. M. N. Nik and M. H. Mahoor, "Explainability of vision transformers: A comprehensive review and new perspectives." arXiv preprint arXiv:2311.06786 (2023). Khalil, Mahmoud, Ahmad Khalil, and Alioune Ngom. "A comprehensive study of vision transformers in image classification tasks." arXiv preprint arXiv:2312.01232 (2023). K. Al-Hammuri, M. Alhaj, M. Alsmirat and A. Al-Ayyoub, "Vision transformer architecture and applications in digital health: a tutorial and survey." Visual computing for industry, biomedicine, and art , Vol. 6, no. 1, Art. no. 14, 2023. Y. Yang, Y. Wu, M. Wang, H. Hu, L. Lin and B. Du, "Transformers meet visual learning understanding: A comprehensive review." arXiv preprint arXiv:2203.12944 (2022). Maurício, José, Inês Domingues, and Jorge Bernardino. "Comparing vision transformers and convolutional neural networks for image classification: A literature review." Applied Sciences Vol. 13, no. 9, Art. no. 5521, 2023. L. Papa, G. Fiameni, L. Rundo, C. Distante and I. M. Ray, "A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking." IEEE transactions on pattern analysis and machine intelligence Vol. 46, no. 12, pp. 7682–7700, 2024.