Image classification of pork primal cuts using an enhanced efficientnet architecture

Automatic classification of pork primal cuts holds significant application value in the food processing industry. To improve the accuracy of pork primal cut image classification, this paper proposes a classification model based on an enhanced EfficientNet-B0 architecture. Firstly, a pork primal cut dataset is constructed, covering various common cuts such as pork belly and pork loin. Secondly, to improve the performance of existing models, a Dynamic Channel Attention Module (DynaCAM) is designed and integrated into EfficientNet-B0. Experiments are then conducted on the self-constructed pork primal cut dataset and miniImageNet. The results demonstrate that the enhanced model achieves optimal classification performance on the pork primal cut dataset while maintaining low parameter count and computational complexity. Although its performance on miniImageNet is inferior to certain large-scale models, considering both accuracy and efficiency, the proposed method still possesses significant application potential and provides new insights for the intelligent upgrade of the food processing industry.