Image classification algorithm based on deep learning

With the rapid development of computer vision technology, image classification algorithm based on deep learning has become a hot research and application field. At present, the mainstream image classification algorithms mainly rely on convolutional neural networks (CNNs) and their variants, which show excellent performance on large-scale image data sets such as ImageNet, effectively solve the problem of feature extraction in image recognition, and greatly improve the classification accuracy. However, the existing algorithms also face some challenges and shortcomings, such as weak generalization ability for small sample data sets, high model complexity leading to large consumption of computing resources, and poor robustness against occlusion or deformation. To address the above challenges, this paper proposes an improved image classification algorithm based on deep learning. The new algorithm enhances the model's ability to learn key features by introducing attention mechanism, and adopts transfer learning strategy to improve the classification performance in the case of small samples. In addition, we also optimize the model structure and reduce the number of unnecessary parameters, thus reducing the computational cost and improving the processing speed. The experimental results show that the proposed algorithm not only significantly improves the recognition accuracy of occlusions or deformities, but also greatly reduces the requirement of computing resources while maintaining high accuracy. Compared with traditional methods, the proposed algorithm has stronger adaptability and higher efficiency, especially in the case of processing limited training data. This research provides new ideas and technical support for further improving the practical application value of image classification algorithm.