FoodVision
Project Overview
About FoodVision
FoodVision is an advanced deep learning-based food detection system that leverages the power of YOLOv8 to identify and classify various food items in images. The system is designed to detect 55 different food classes, with a particular focus on fruits and vegetables, making it a valuable tool for dietary monitoring and nutritional analysis. The project achieved impressive performance metrics with ~80% accuracy (mAP50), ~80% precision, and ~75% recall.
Key Features
- Real-time food detection using YOLOv8
- Support for 55 different food classes including fruits and vegetables
- Calorie estimation per 100g of detected food items
- Web interface with image upload and camera capture support
- Bounding box visualization with confidence scores
Technical Details
Model Architecture & Implementation
FoodVision uses YOLOv8n (nano version) with the following specifications:
- Input size: 640x640 pixels
- Batch size: 32
- Learning rate: 3e-4
- Training epochs: 45
Food Categories
The system can detect various food items categorized by color:
- Green foods: asparagus, avocados, broccoli, cabbage
- White/Beige foods: banana, cauliflower, garlic, mushroom
- Purple/Red foods: beetroot, blackberry, cherry, eggplant
- Orange/Yellow foods: apricot, carrot, corn, mango