Ingredient-Level Food Image Segmentation for Nutrition Awareness
Quick Answer
This study introduces ingredient-level semantic segmentation using SegFormer-B0 and B1 on the FoodSeg103 dataset, achieving pixel accuracies of 0.7709 and 0.7929, respectively.
Quick Take
This study introduces ingredient-level semantic segmentation using SegFormer-B0 and B1 on the FoodSeg103 dataset, achieving pixel accuracies of 0.7709 and 0.7929, respectively. The B1 model outperformed B0 with a mean IoU improvement of 0.0683, providing a visual summary of ingredient areas for enhanced nutrition awareness.
Key Points
- SegFormer-B0 achieved 0.7709 pixel accuracy on FoodSeg103.
- SegFormer-B1 improved mean IoU by 0.0683, reaching 0.3204.
- The system converts masks into ingredient-area percentages for visual summaries.
- This approach aids nutrition awareness without estimating calories or macronutrients.
- The dataset comprises 2,135 images for testing model performance.
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