## 2 Related Works  
Various image-based and deep learning-based approaches have recently been proposed to automate beef carcass grading. Early studies utilized hyperspectral imaging (HSI) to predict marbling and intramuscular fat (IMF). [PERSON] et al. [13] employed decision tree classifiers using HSI data to predict marbling scores, while [PERSON] et al. [14] utilized three-dimensional principal component analysis (PCA) and local binary pattern (LBP) features to estimate tenderness. However, HSI-based approaches are limited in practical use due to high equipment costs and computational complexity.  
As a result, RGB image-based classification has gained popularity. [PERSON] et al. [15] combined LBP features and Random Forest classifiers to predict marbling grades from loin cross-section images, and [PERSON] et al. [16] quantitatively analyzed marbling areas using thresholding and morphological operations. [PERSON] et al. [17] demonstrated that LBP features and Partial Least Squares regression could predict MSA marbling scores and IMF percentages. [PERSON] et al. [18] also proposed a vision-based system capable of estimating Eye Muscle Area, IMF%, MSA, and AUS-MEAT marbling scores with high accuracy (\(R^{2}\) ranging from 0.70 to 0.83), showing the potential of image-based methods in industrial meat quality assessment.  
Several segmentation-based approaches have also been proposed. [PERSON] et al. [19] applied pixel-wise classification using AlexNet to segment the longissimus muscle region, while [PERSON] et al. [20] evaluated multiple CNN architectures (SegNet, UNet, DeepLab, etc.) for carcass area extraction. [PERSON] et al. [3] proposed MSENet, a multi-task network that performs both segmentation and marbling score regression simultaneously. [PERSON] et al. [6] used DeepLabV3+ and a custom CNN to detect anatomical keypoints and then applied multivariate regression to estimate longissimus muscle parameters (LMP).  
However, these approaches are based on two-stage pipelines that separate segmentation and classification, increasing training and inference complexity. Unlike previous studies that adopt a two-stage pipeline to enhance regional interpretability or enable multi-task learning (e.g., ROI segmentation followed by quality estimation), our one-stage approach aims to improve deployment efficiency by eliminating intermediate processing. As shown in our experiments (Section 4.1), the proposed model achieves higher classification accuracy and significantly faster inference speed. This highlights that, while two-stage methods offer modular benefits, they may not be optimal for real-time grading systems that prioritize speed and simplicity over anatomical localization.  
To address this issue, recent studies have focused on one-stage end-to-end models that omit segmentation. [PERSON] et al. [21] performed part classification using CNNs on RGB images taken from boning lines. [PERSON] et al. [22] used conveyor-mounted camera systems to predict IMF% with an \(R^{2}\) of 0.87. [PERSON] et al. [23] proposed a smartphone-based VIA application to classify SEUROP grades quantitatively.  
The EfficientViT model used in this study is a lightweight vision transformer architecture that achieves both high accuracy and fast inference. [PERSON] et al. [24] reported up to 13\(\times\) fewer computations and more than 6\(\times\) faster inference compared to SegFormer by leveraging Multi-Scale Linear Attention. [PERSON] et al. [10] further improved performance with Cascaded Group Attention and a Sandwich Layout, achieving 2.6\(\times\) faster inference speeds while maintaining accuracy comparable to EfficientNet.