## 5 Discussion  
This study investigated the structural advantages of the proposed EfficientViT-based one-stage model for beef carcass grading, comparing its performance against conventional CNNs and two-stage approaches through extensive experiments. EfficientViT combines convolutional layers with a multi-scale linear attention mechanism, forming a hybrid architecture capable of effectively capturing both local texture features and global visual patterns. Notably, it achieved the highest F1 score and precision in predicting the highest-quality grade (1++), indicating that the attention mechanism is particularly beneficial for identifying distributed marbling patterns, which are critical for this class. In experiments stratified by loin cross-section ratio, EfficientViT consistently maintained high recall, not only in the over 20% group with abundant meat area but also in the under 10% group, where visual information was limited. This suggests that the model's attention structure enables it to integrate sparse yet meaningful features from across the entire image, demonstrating robust performance, regardless of ROI size. Moreover, visualization results using Grad-CAM and attention maps highlighted a clear difference in spatial focus: CNN-based models tended to concentrate on specific local regions, while EfficientViT displayed a more evenly distributed attention pattern across the image. To further quantify the spatial extent of model attention, we measured the proportion of each image that received high activation (defined as scores \(\geq 0.5\) in Grad-CAM or attention map). Table 8 presents descriptive statistics for this high-activation area across all one-stage models. EfficientViT exhibited the largest activated region, with a mean value of 49.78% and an interquartile range (IQR) spanning 39.42% to 59.91%. In contrast, the CNN-based models showed significantly narrower activation ranges: DenseNet-121 averaged 13.79%, \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **1++ Accuracy (\%)** & **F1 Score** & **Precision** & **Recall** \\ \hline ResNeXt-50 & 99.01 & 0.9835 & 0.9917 & 0.9754 \\ DenseNet-121 & 99.26 & 0.9879 & 0.9760 & **1.0000** \\ VGG-16 & 98.77 & 0.9794 & 0.9835 & 0.9754 \\ EfficientViT & **99.75** & **0.9959** & **1.0000** & 0.9918 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of EfficientViT and CNN models on 1++ grade prediction (over 20% loin area). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Models** & **1++ Accuracy (\%)** & **F1 Score** & **Precision** & **Recall** \\ \hline ResNeXt-50 & **98.38** & **0.9686** & **0.9702** & 0.9670 \\ DenseNet-121 & 98.17 & 0.9644 & 0.9684 & 0.9604 \\ VGG-16 & 98.12 & 0.9638 & 0.9622 & 0.9653 \\ EfficientViT & 98.29 & 0.9671 & 0.9639 & **0.9703** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of EfficientViT and CNN models on 1++ grade prediction (under 10% loin area). ResNeXt-50 10.14%, and VGG-16 only 3.35%. This result suggests that EfficientViT tends to consider broader global contexts when making predictions, which aligns with its multi-scale attention architecture. Interestingly, the ranking of average activation area--EfficientViT > DenseNet-121 > ResNeXt-50 > VGG-16--matches the models' classification performance on the subset of samples with over 20% loin area (see Section 4.3). This supports the hypothesis that broader attention coverage is positively associated with robustness to spatial variation in beef cross-section images. These findings reveal fundamental differences in feature learning strategies and emphasize the importance of considering model architecture when explainability and spatial reasoning are required. While the two-stage models benefited from explicit ROI segmentation, reducing irrelevant information and improving classification accuracy, they were computationally intensive and exhibited longer inference times.