# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 1 Introduction  
Pet breeds are one of the most concerning topics in people's daily lives, especially cats and dogs, which are the most common household pets. With rapid advances in technology, people can obtain highly accurate breed identification results using genetic testing [1]. However, such work requires obtaining genetic samples such as pet hair and blood, and sending them to online or offline pet genetic testing institutions for identification in order to obtain the final results. This process is often both time-consuming and labor-intensive, making it difficult to adapt to the rapid advancement of the \"lazy economy\". Nowadays, people increasingly desire the ability to fulfill their needs anytime and anywhere, including food delivery, online meetings, and educational opportunities. Therefore, it is of practical significance to offer a mobile pet breeds identification system that is not constrained by time and location. Over the past few years, deep learning, particularly convolutional neural networks, has been extensively applied across numerous fields [2; 3; 4; 5]. Deep learning models can automatically extract effective features from supervised data end to end. In the realm of image classification, it has become a widely accepted fundamental approach to extract image features via convolutional layers [6]. The vast majority of the existing research on image classification [7; 8; 9; 10; 11; 12; 13] focuses on classifying data that are visually recognizable. Even for datasets as classic as ImageNet [7], its category definition is based on species or item types (such as cats, pencils, trees, and cars), rather than breeds. In addition, many of the research subjects in these works are situated in ideal environments. Consequently, the algorithm exhibits low classification accuracy in real-world scenarios, making it challenging to meet customer expectations. This is due to the variableand dynamic scenes in practical applications, where the size of the research subjects in the image is variable, the image background is complex, and there are often accompanying distractors. Images in which a pet occupies a larger area tend to favor large kernel convolutions, while those in which a pet occupies less space prefer small kernel convolutions, bringing significant challenges to the model. In practical applications, due to the reduced signal-to-noise ratio in the visual features that potentially distinguish target from background [14], the model often struggles to extract useful features related to the task, leading to consistently low recognition accuracy. This issue is pervasive across the entire field of image processing, not solely confined to our pet breeds classification endeavors. We consider that there are two reasons. Firstly, in terms of the practical application data, it is difficult for models to extract effective features from complex images due to the low signal-to-noise ratio of feature maps. Secondly, the feature extraction ability of the model is insufficient, which is not only due to performance limitations stemming from the model design, but also often because the data fed during the training process are so simple that they have a high signal-to-noise ratio of image features. The model becomes accustomed to feature maps with a high signal-to-noise ratio in the training process. Consequently, it often encounters difficulty in adapting to practical applications where the acquired images have a low signal-to-noise ratio. In addition, in practical applications, due to time and human resource constraints, the dataset used for fine-tuning may not necessarily achieve an optimal distribution. Therefore, it is crucial for the model to be able to adapt to datasets with suboptimal distributions and effectively utilize them for transfer learning. In response to these issues, we conducted a selection process within the Oxford-IIIT Pet dataset [15], selecting images with more intricate backgrounds, where pets occupy varying sizes of regions.