A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments

Most existing studies on pet breeds classification focus on images with simple backgrounds, leading to the unsatisfactory performance of models in practical applications. This paper investigates training pet breeds classification models using complex images and constructs a dataset for identifying breeds of pet cats and dogs. We use this dataset to fine-tune three SOTA models: ResNet34, DenseNet121, and Swin Transformer. Specifically, in terms of top-1 accuracy, the performance of DenseNet is improved from 89.10% to 89.19%, while that of the Swin Transformer is increased by 1.26%, marking the most significant enhancement. The results show that training with our dataset significantly enhances the models’ classification capabilities in complex environments. Additionally, we offer a lightweight pet breeds identification model based on PBI-EdgeNeXt (Pet Breeds Identification EdgeNeXt). We utilizes the PolyLoss function and Sophia optimizer for model training. Furthermore, we compare our model with five commonly used lightweight models and find that the proposed model achieves the highest top-1 accuracy of 87.12%. These results demonstrate that the model achieves high accuracy, reaching the SOTA level.