Developers Build AI-Powered Calorie Estimator Using SAM and GPT-4o Vision Models

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A new developer tutorial demonstrates how to build a precise nutritional analysis tool by combining Meta's Segment Anything Model (SAM) with OpenAI's GPT-4o multimodal API. The system first uses SAM to generate individual segmentation masks for each food item on a plate, providing spatial context before passing the data to GPT-4o for calorie and macro estimation. Unlike traditional diet-tracking apps, this pipeline can distinguish between multiple food items in a single image and estimate portion sizes more accurately. The tutorial covers the full technical stack, including Python, PyTorch, OpenCV, and FastAPI, making it suitable for production deployment. The resulting API allows a mobile or web frontend to upload a meal photo and receive a structured JSON breakdown of calories, protein, fats, and carbohydrates in real time.

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