Food Calorie Prediction App
85%+ classification accuracy
Food Calorie Prediction emerged from a simple desire: nutrition tracking shouldn't be guesswork. Fueled by the power of machine learning, I created an app where every meal tells its story - food recognized, calories revealed, health made effortless through the lens of AI.
1 / 285%+ classification accuracy
SnapshotAt a glance
ML Engineer
2 months
85%+ classification accuracy
Technologies Used
5 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Make calorie tracking effortless through AI-powered food recognition.
Core Process
The process of Developing it.
Trained CNN models on food datasets, integrated calorie database, built Streamlit interface.
Build notesWhat I built
03Trained CNN models using TensorFlow and Keras on diverse food image datasets, achieving 85%+ classification accuracy by implementing data augmentation techniques to handle various lighting conditions, food varieties, and presentation styles, making nutrition tracking effortless through AI-powered recognition.
Integrated a comprehensive calorie database with the classification system, enabling automatic calorie estimation based on recognized food items, creating a seamless workflow where food images are analyzed and nutritional information is instantly revealed.
Built a user-friendly Streamlit interface with Docker deployment capabilities, allowing users to upload food images and receive instant calorie predictions, packaged as a containerized application ready for production deployment with minimal setup requirements.
Helping people track nutrition through automated food recognition.
Features
- Food image classification
- Calorie estimation
- User-friendly interface
- Docker deployment ready
Challenges
- Food variety and lighting conditions
- Accurate calorie estimation
