Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.

This study presents a robust approach for continuous food recognition essential for nutritional research, leveraging advanced computer vision techniques. The proposed method integrates Mutually Guided Image Filtering (MuGIF) to enhance dataset quality and minimize noise, followed by feature extraction using the Visual Geometry Group (VGG) architecture for intricate visual analysis. A hybrid transformer model, combining Vision Transformer and Swin Transformer variants, is introduced to capitalize on their complementary strengths.