Upload README.md

README.md

@@ -6,91 +6,4 @@ 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.
+short_description: sch proj