## 1 Introduction  
Beef quality grading is a crucial process that directly affects pricing and significantly influences consumer choices [1]. Higher-grade beef commands a substantially higher price compared to lower-grade products [2]. In South Korea, beef carcasses are graded based on five criteria: intramuscular fat (IMF), meat color, fat color, texture, and maturity. The carcasses are categorized into five grades: 1++, 1+, 1, 2, and 3 [2]. The current standard grading method relies on visual inspection by trained experts [3].  
However, such manual assessments are inherently subjective and often lack consistency among evaluators, posing limitations in terms of efficiency and reliability in large-scale industrial environments [4]. To overcome these challenges, image-based analysis techniques and deep learning approaches have been actively explored to automatethe grading process [5]. Early automated systems relied on complex preprocessing steps such as illumination correction, background removal, and region-of-interest (ROI) extraction to achieve acceptable accuracy, but these multi-step pipelines are inefficient for practical deployment.  
To reduce preprocessing burdens, several studies have proposed segmentation-based methods to automatically extract ROI regions and improve classification accuracy [3]. However, the two-stage structure that separates segmentation and classification increases model complexity and is less efficient in terms of end-to-end learning.  
Recently, one-stage classification models that directly predict grades from entire images without segmentation have gained attention [6]. However, conventional CNN-based one-stage models tend to focus on local features, which limits their performance when global visual patterns--such as marbling distribution in IMF--are critical. Intramuscular fat is known to be highly influential and closely correlated with other grading criteria [7; 8; 9].  
To address these limitations, this study proposes a one-stage beef carcass grading model based on EfficientViT, a lightweight hybrid architecture that combines Vision Transformers and CNNs. EfficientViT is designed to simultaneously capture local patterns via convolutional layers and global contextual features via self-attention, while maintaining a compact and fast-inference design suitable for real-time deployment [10].  
The main contributions of this study are as follows:  
1. We propose a high-accuracy one-stage grading model based on EfficientViT that eliminates the need for a complex two-stage pipeline;
2. We demonstrate the importance of leveraging global features through performance comparisons with CNN-based models (VGG16, ResNeXt50, DenseNet121);
3. We enhance model explainability through visual analysis using Grad-CAM [11] and attention maps [12];
4. We validate the model's suitability for real-time industrial applications with low inference latency and compact parameter size.  
This paper details the architecture of the proposed EfficientViT-based one-stage model and evaluates its superiority through quantitative and qualitative comparisons with existing methods. In particular, we conduct an in-depth analysis of the model's performance in predicting the highest grade (1++) and its robustness to variations in loin area proportion within images.  
To summarize, this study aims to develop a lightweight and accurate one-stage beef carcass grading model using EfficientViT, a vision transformer architecture capable of capturing both local and global features. We compare its performance with conventional CNN-based classifiers and two-stage segmentation-classification pipelines. The rest of this paper is organized as follows: Section 2 reviews related works; Section 3 describes the dataset, models, and experimental settings; Section 4 presents experimental results and analysis; Section 5 discusses the findings and limitations; and Section 6 concludes the study.