Cow face recognition based on transformer group

This paper found that in existing cattle face recognition algorithms, convolutional neural networks (CNN) are more commonly used. The processing speed and performance of CNNs in processing large data are often limited by the receptive field and the number of network layers. At the same time, the visual transformer has achieved great success due to its high performance and excellent ability to process large data. Therefore, this paper borrows the traditional cattle face recognition process, using the DETR algorithm for object detection and the Swin-transformer algorithm for image classification, thereby improving the running efficiency and enhancing the ability to process large data. Comprehensive experiments show that the algorithm in this paper is superior to existing traditional models, achieving a good balance between speed and accuracy. Compared with traditional algorithms, this algorithm utilizes the emerging Transformer group, and not only can effectively identify the unique identity of cattle's front face, but also has good effect on identifying the unique identity of cattle's side face and rear face due to using the Transformer group to train and test a large amount of data.