## 5 Conclusions  
### Summary of Experimental Results and Implications  
The experimental results showed that the vision transformer (ViT) model outperformed both ResNet50 and CNN models. This is due to the fact that transformer-based models, like ViT, offer greater scalability compared to CNN-based models. This result supports previous studies comparing the analysis performance of ViT and CNN variants [20,21]. In this study, ViT demonstrates a relatively lower loss rate, which highlights its ability to analyze images with complex fashion items effectively. Specifically, ViT directly utilizes the transformer structure, which requires fewer computational resources during transfer learning compared to CNN variants--such as ResNet and CNN in this study. On the other hand, CNN-based models tend to require larger datasets due to their increasingly deep architectures. This study does not consider the analysis speeds of each model, as these can vary depending on experimental conditions, including virtual GPU performance, network capabilities, and other factors. The dataset used in this study may not have been large enough to fully train these models, suggesting that dataset size plays a significant role in model performance.  
For academic implications, first, the correlation between dataset size and model performance should be stressed. This study revealed that dataset size can be considered as a meaningful factor in determining model performances and choosing more proper models. This suggests that in future research, CNN-based models may demonstrate improved performance when larger datasets are used. This study also supports the study of [PERSON] et al. to illustrate the higher performance of ViT for image classification than traditional CNN [22].  
Second, the scalability of transformer architectures. The strong performance of ViT demonstrates the scalability and efficiency of transformer structures, indicating the need for further research applying transformer-based models to diverse datasets and environments. Transformer architectures have the potential to be applied to various data types, highlighting their potential for broader use in future studies.  
Third, this study suggests the academic consideration to consider the capability of ViT, which still is a newer algorithm. This study can be academic evidence of the superior performance of ViT.  
For practical implications, first, the efficiency of transformer-based models can be considered for the business performances. Vision transformer (ViT) uses fewer computational resources than CNN during transfer learning, making it more efficient for practical applications [22]. In particular, ViT demonstrates strong performance even when using limited datasets, making it an effective solution in industrial settings where data resources are scarce. With the ViT algorithm, small and medium-sized corporations can be supported to enhance their business performances, especially business automation and recommendations for the customers.  
Second, the importance of dataset size. CNN-based models, due to their increasingly deep architectures, require large datasets. Therefore, when applying these models in practice, careful consideration must be given to the relationship between dataset size and model learning efficiency. Insufficient datasets can hinder the performance of CNN models.  
Third, SMEs (small and medium-sized enterprises), including the fashion industry, should face the comparatively technological handicaps of big companies because of the lack of manpower, investment abilities, understanding levels on convergence technologies, and others. According to the research results, ViT and other image processing algorithms may help the handicapped SMEs to save time, salaries, and investment portfolios. For instance, with ViT as this study, fashion SMEs can reduce the spending time to realign and combine the fashion items.