Improving Image Classification Using Attention Mechanisms in Deep Learning Models

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts.