Application of MindSpore-based waste classification detection technique

This paper combines deep learning technology and, based on the MindSpore framework, designs and implements an efficient algorithm for waste classification. The paper begins by introducing the background of waste classification and its importance in environmental protection and resource recycling. At the same time, MindSpore offers convenience for the construction, training, and efficient deployment of algorithms and models. To meet the demands of waste classification tasks, the paper builds a specialized waste classification dataset and selects a series of mainstream image classification algorithms. During the model training process, data augmentation techniques such as random cropping, rotation, and horizontal flipping are introduced to enhance the model's recognition capability for various waste images. In terms of algorithm optimization, the paper selects appropriate loss functions and optimizers. The SE attention module is introduced with the aim of improving the model's ability to capture and learn key features in waste images, thereby increasing the classification accuracy. Through ablation studies and comparative experiments, the effectiveness of the SE module is validated, and the impact of different algorithms and optimizers on model performance is analyzed. The experimental results provide strong support and validation for the proposed waste classification algorithm.