## 1 Introduction  
Among the most significant challenges in computer vision is fine-grained image recognition, which seeks to distinguish objects from different sub-categories of a particular super-category, for instance, different consumer product categories, vehicle models, bird species, etc. In computer vision, there are numerous fine-grained image recognition applications, including fine-grained image retrieval [1], visual-based recommended systems [2, 3], picture generation [4], visual search system [5], and image labelling [6]. Therefore, fine-grained image recognition is a key research topic as well as an actively emerging area of image recognition. Even though networks based on deep learning are capable of extracting important features [7], particularly CNNs [5, 8], fine-grained classification is still a difficult task that needs learning to differentiate fine image features. As a result, there has always been a spotlight on learning desired features regarding both fine and discriminating information. Present fine-grained image recognition approaches can usually be sorted as weakly supervised and strongly supervised. Weakly supervised methods gather specific local areas for part localization using just image labels, whereas strongly supervised methods train a network using extra information such as bounding boxes, image labels, and manual annotation [9; 10; 11; 12; 13; 14; 15]. In weakly supervised learning, attention-based approaches are becoming more prevalent in recent times given the ability to perform end-to-end training without additional information. Convolutional neural networks are used in attention-based approaches to construct a local sub-network that gathers important parts of the image. After that, an additional sub-network is utilized to achieve recognition at the output. These methods, however, come with certain acknowledged drawbacks. The amount of object parts must be addressed; for example, the object parts are limited and predefined, which restricts the model's efficiency and adaptability. Furthermore, constructing and training sub-networks to handle every attention element in an object is unreliable, resulting in bottlenecks within the structure. Additionally, local regions could be concatenated but cannot affect the connection between several local regions from a global perspective, which is also extremely important for fine-grained image recognition. Such restrictions need the development of a reliable model capable of extracting unrestricted main features, such as coarse-grained along with fine-grained (attention) features in a relational manner. The vision transformer [16] recently gained incredible results in the recognition task, proving that using a simple transformer aligned to a series of image patches is capable of capturing the relevant regions because of its inherent attention mechanism. A number of expanded research projects targeting related tasks, including semantic segmentation [17; 18] and object detection [19], demonstrated its capacity to extract local features as well as global features. The transformer's capabilities make it suitable for fine-grained image recognition, considering the initial distant receptive field [16], which allows it to track down minimal differences and associated spatial relationships within the initial layers. Convolutional neural networks, on the other hand, primarily leverage the image localization feature and simply locate weaker distant relationships in highly dense layers. Moreover, minor differences among fine-grained categories appear only in particular regions; it is unsuitable to construct a filter that notices minute differences across every region of an image. The main idea of our research is to investigate the fusion of the vision transformer with the one-stage object detector and their performance in fine-grained image recognition, as there are few studies proving the viability of one-stage object detectors in the fine-grained recognition problem.