The evolution of deep learning has been marked by groundbreaking architectural innovations that continually push the boundaries of computer vision performance. Convolutional Neural Networks (CNNs) have long been the gold standard for image analysis, featuring localized filters that efficiently capture spatial features. However, Vision Transformers (ViTs) have emerged as a formidable alternative, applying the self-attention mechanism popularized by natural language processing to image understanding. CNNs excel at capturing local spatial hierarchies through convolutional operations, making them computationally efficient and particularly effective with limited training data. In contrast, Vision Transformers process images as sequences of patches and leverage global self-attention, enabling them to capture long-range dependencies across the entire image. Empirical evidence demonstrates that ViTs achieve superior performance on large-scale datasets, often surpassing traditional CNN architectures in accuracy metrics. However, CNNs remain more parameter-efficient and require less data for effective training. The choice between these architectures depends on specific application requirements, computational resources, and dataset size. Hybrid models combining the strengths of both approaches are increasingly gaining traction in the research community, suggesting that the future of computer vision may not involve choosing one architecture over another, but rather intelligently combining their complementary strengths.
