Advancements in Image Recognition: Comparing CNNs and Vision Transformers

Abstract. This paper explores advancements in image recognition technologies, highlighting the shift from conventional methodologies to contemporary deep learning techniques, specifically focusing on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The study examines key architectures including CNNs, and various Transformer-based models, analyzing their performance evaluating their effectiveness in diverse tasks such as image classification, object detection, and facial recognition. The research highlights the strengths and limitations of CNNs and ViTs, focusing on their ability to handle complex and diverse datasets. A detailed comparative analysis is conducted, emphasizing performance metrics, robustness, and adaptability across different image recognition scenarios. The results reveal that while CNNs excel in traditional image processing tasks, ViTs demonstrate significant improvements in capturing long-range dependencies, thereby enhancing recognition accuracy in more complex contexts. This analysis offers critical perspectives on selecting and applying image recognition models, guiding future exploration and practical use in various industries. It underscores the impact of deep learning innovations on advancing image recognition capabilities and highlights potential directions for ongoing development in the field.