TATS is a comprehensive food analysis application that allows users to analyze their meals through image recognition. The application uses custom-trained machine learning models to identify and categorize different types of food.
The project consists of three main components:
- 🤖 AI: Machine learning models for food classification
- 🖥️ Backend: Firebase Functions and Firestore database
- 📱 Mobile: React Native mobile application (iOS and Android)
- 🍕 Food image recognition and classification
- 📊 Meal analysis and categorization
- 📜 History tracking of analyzed meals
- 📱 Cross-platform mobile application
- Custom-trained YOLOv5 models for food classification
- Deployed via Cerebrium
- Firebase Functions
- Firestore database
- Node.js 22
- React Native / Expo
- Firebase integration
- Zustand for state management
- React Navigation
The project includes several specialized models for food classification:
- 🍛 Main dish model
- 🥣 Soup model
- 🍖 Meat dish model
- 🥗 Vegetarian dish model
- 🥤 Beverage model
- 🍰 Dessert model
- 🍟 Side dish model
- ➕ And more...
🚀 TATS provides a seamless food analysis experience, helping users track and understand their meals with AI-powered insights!
- Alperen Gözüm
- Serhat Derya
- Gökhan Güney
- Alper İşleyen
- Emre Salaman
- Yavuz Mert Bozkurt