# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 4 Experiment and Analysis
### Discussion  
In this research, we introduce a method that utilizes transfer learning to enhance the accuracy of pet breeds classification in complex backgrounds. To develop this method, we create a specialized dataset for pet breeds identification in complex backgrounds, referred to as the CPI dataset. This dataset is derived from the Oxford-IIIT Pet dataset, with images chosen from those with complex backgrounds, interference, or redundant information. We employ three different architectures models pretrained on ImageNet, fine-tune them using the CPI dataset and the Oxford-IIIT Pet dataset, and subsequently evaluate their performance on the same test set of complex images. The experimental results indicate that the Top-1 accuracy of the models fine-tuned on the CPI dataset is higher than that on the Oxford-IIIT Pet dataset as shown in Table 2. This suggests that our CPI dataset is better suited for models that operate in real-world environments. A more detailed analysis is discussed in the \"Experiments and Analysis\" section, so we will not expand on it here. Meanwhile, we propose a new lightweight model termed PBI-EdgeNeXt, designed to address the challenges of pet breeds identification in complex backgrounds. This model combines EdgeNeXt with octave convolution, enabling it to effectively capture task-specific features while suppressing irrelevant and redundant information. As described in the \"Ablation Experiments\" subsection above, the validity of our improvements to EdgeNeXt is confirmed through detailed ablation studies, as shown in Table 3. Our PBI-EdgeNeXt model demonstrates outstanding performance on the CPI dataset, outperforming the other five prevalent lightweight SOTA models (as depicted in Table 4). This indicates that our model design not only achieves theoretical breakthroughs but also has significant advantages in practical applications, providing a new technical path and practical value for solving the problem of pet breed recognition in complex backgrounds. Finally, we employ the saliency map visualization to display the feature extraction of selected images from the CPI dataset using PBI-EdgeNeXt (see Figure 6). This visualization demonstrates its capacity to suppress redundant information and capture task-relevant features within the images. This visualization not only helps us understand the working mechanism of the model in complex scenarios but also intuitively shows its potential and effectiveness in practical applications. We also select some representative complex images from the test set, use six models to extract the features of these images, and display them using saliency map visualization techniques. We select three of the most representative results as shown in Figure 7. This visualization reveals the success of the proposed PBI-EdgeNeXt model and the reasons for the failure of some comparative models. Overall, our research not only introduces new datasets and model designs for the field of pet breed identification but also provides in-depth theoretical exploration and empirical analysis for image classification problems in complex backgrounds. These achievements  
Figure 7: Saliency maps comparison of six models. This figure shows how much attention the models attach to the information in these images, and visually reveals the reasons for the failure of some comparative models. are not only of great significance in academic research but also provide powerful technical support and guidance for the industrial sector in practical applications, thereby promoting progress and development in related fields. However, our pet breed identification system has certain limitations. The biggest limitation is that the proposed PBI-EdgeNeXt, as a classification model, can only output one definite classification result for an image. Compared to object detection models and instance segmentation models, our model is unable to classify pet breeds for multiple instances in one image.