Image recognition based on CNN-transformer algorithm: evidence from CIFAR-10 demonstration

Image recognition plays a crucial role in various applications such as autonomous driving and medical diagnostics. While CNNs and Transformers have shown significant advancements, their individual limitations in handling complex images prompted the exploration of hybrid models. This study empirically examines the application of CNN-Transformer hybrids on the CIFAR-10 dataset. Through comparative experiments, we demonstrate that the CNN-Transformer model achieves superior accuracy compared to standalone CNN and Transformer models, despite higher computational costs. This highlights the effectiveness of combining local feature extraction and global reasoning for enhanced image recognition performance.