Fine-grained Image Classification Algorithm Based on Channel Attention Enhancement

Image classification, as a fundamental task in the field of computer vision, always occupies an important strategic position in the development of artificial intelligence technology. In this paper, a method based on the self-attention mechanism is proposed for the fine-grained classification problem. By embedding the Squeeze-and-Excite module in the MobileNet architecture, the weights of different feature channels are dynamically adjusted to efficiently capture the detail information of key regions. Experimental results show that the method achieves some performance improvement on the Stanford Cars dataset from Stanford University, especially in recognizing difficult vehicle models in complex scenes. This research provides an efficient and reliable solution for fine-grained classification, and also demonstrates the potential of the self-attention mechanism in practical applications.