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README.md

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+title: FoodVision
+emoji: 📈
+colorFrom: purple
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.40.2
+app_file: app.py
+pinned: false
+short_description: sch proj
+
+# FoodVision: Automated Food Detection Using YOLOv8
+
+## Project Overview
+FoodVision is a deep learning-based food detection system that utilizes YOLOv8 to identify and classify various food items in images. The system is capable of detecting 55 different food classes with a focus on fruits and vegetables, making it useful for dietary monitoring and nutritional analysis.
+
+## Features
+- Real-time food detection using YOLOv8
+- Support for 55 different food classes
+- Calorie estimation per 100g of detected food items
+- Web interface using Streamlit
+- Support for both image upload and camera capture
+- Bounding box visualization with confidence scores
+
+## Model Architecture
+- Base model: YOLOv8n (nano version)
+- Input size: 640x640 pixels
+- Batch size: 32
+- Learning rate: 3e-4
+- Training epochs: 45
+
+## Performance Metrics
+- mAP50: ~0.8 (80% accuracy at 50% IoU)
+- Precision: ~0.8
+- Recall: ~0.75
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone [email protected]:2302660/aai3001_final_project.git
+cd aai3001_final_project
+```
+
+2. Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+1. Run the Streamlit application:
+```bash
+streamlit run Sapp.py
+```
+
+2. Use the web interface to:
+   - Upload images or capture them using your camera
+   - View detected food items with bounding boxes
+   - See confidence scores and calorie information
+
+## Project Structure
+```
+.
+├── Model.ipynb         # Notebook for model training and evaluation
+├── cal.py              # Core calorie calculation and detection functions
+├── Sapp.py             # Streamlit web application
+├── best.pt             # Trained model weights (not included in repo)
+└── README.md           # Project documentation
+```
+
+## Supported Food Classes
+The model can detect 55 different food items including:
+- Green foods: asparagus, avocados, broccoli, cabbage, etc.
+- White/Beige foods: banana, cauliflower, garlic, mushroom, etc.
+- Purple/Red foods: beetroot, blackberry, cherry, eggplant, etc.
+- Orange/Yellow foods: apricot, carrot, corn, mango, etc.
+
+## Live Demo
+You can try out the live demo at:
+- [Hugging Face Space](https://nightey3s-aai3001-final-project.hf.space/)
+- [Project Files](https://huggingface.co/spaces/nightey3s/aai3001_final_project/tree/main)
+
+## Team Members
+- Brian Tham
+- Hong Ziyang
+- Javier Si Zhao Hong
+- Timothy Zoe Delaya
+
+## Course Information
+AAI3001 Deep Learning and Computer Vision, Trimester 1, 2024
+Singapore Institute of Technology
+
+## Future Work
+- Expand the dataset to include more food categories.
+- Implement portion size estimation.
+- Compare uploaded food images with dietary recommendations.