A dietary intelligence system designed to identify food items from visual data and predict nutritional content. Built to assist in automated dietary tracking and health monitoring.
The Objective
In an era of health-conscious living, manual tracking of caloric intake is inefficient. The objective was to deploy a neural network capable of instantly recognizing food items and retrieving their nutritional profile.
Technical Architecture
The core of the system is a Convolutional Neural Network (CNN) developed using TensorFlow. The model was trained on a diverse dataset of food imagery to ensure resilience against varying lighting and angles.
- Frontend: Simple HTML/CSS interface for image upload.
- Backend: Flask API to process requests and serve predictions.
- Model: MobileNetV2 architecture for efficient edge deployment.
"Accuracy is not just a metric; it's the difference between a healthy choice and a miscalculation."
Outcome
The system achieves high accuracy on standard food datasets and serves as a foundational module for smarter health apps.