Implementation of Swin Transformer for Web-Based Identification of Rhizome Shaped Spices Using Streamlit Framework
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Abstract
Indonesia is a country rich in natural resources, particularly in various types of spices. Among these, rhizome-shaped spices such as ginger, turmeric, galangal, and aromatic ginger often present classification challenges due to their similar visual appearances. This study aims to address this issue by developing a web-based identification system utilizing the Swin Transformer—an advanced Vision Transformer architecture known for its effectiveness in image classification tasks. The Swin Transformer model demonstrated superior performance, achieving an accuracy of 99.11%, precision of 98.24%, recall of 98.21%, and F1-score of 98.21%. These results significantly outperform the Xception convolutional neural network (CNN) model, which was previously considered state-of-the-art, with 95.00% accuracy, 90.14% precision, 90.00% recall, and 90.01% F1-score. To ensure practical usability, the final Swin Transformer model was deployed as a web application using the Streamlit framework, allowing users to classify rhizome spices through image uploads. These findings highlight the effectiveness of Swin Transformer for practical image-based spice classification.
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