Image recognition based on self-distillation and spatial attention mechanism

As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key component of computer vision due to their superior performance in automatically detecting image features, handling high-dimensional data and performing large-scale classification tasks. This paper examines the development of CNNs, leveraging the strengths of current mainstream image recognition methods, and proposes a Self-Distillation and Attention-based Convolutional Neural Network (SDACNN) model to further enhance CNN accuracy. Experimental results demonstrate that the proposed model effectively accomplishes image recognition tasks.