🥦🍎 Fruits & Vegetables Detection App

The Fruits & Vegetables Detection App is a Flask web application powered by a YOLOv5 deep learning model trained with PyTorch.
It detects 32+ classes of fruits and vegetables from images or webcam input and provides calorie information for each item.

This project highlights skills in Computer Vision, Flask Web Development, and AI-powered applications.

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✨ Features

• 🖼️ Upload Image Detection – Upload an image and get bounding boxes with detected fruits/vegetables.
• 🎥 Live Webcam Detection – Real-time detection directly in the browser.
• 📦 32+ Supported Classes – Apple, Banana, Tomato, Mango, Potato, Strawberry, and more.
• 🔎 YOLOv5 Model – Custom-trained weights (best.pt) for accurate detection.
• 📊 Calorie Information – Each detected item shows nutritional values.
• 🎨 Flask + HTML Templates – Lightweight UI with Bootstrap/Tailwind-ready design.

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🛠️ Tech Stack

• PyTorch – Deep learning framework
• YOLOv5 – Object detection model
• Flask – Web framework for serving the app
• OpenCV / NumPy – Image processing utilities
• HTML + Jinja2 – Templates for UI rendering

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/your-username/fruits-vegetables-detection.git
cd fruits-vegetables-detection

2. Install Dependencies

pip install -r requirements.txt

4. Run the Flask App

python app.py
The app will start on http://127.0.0.1:5000/

📂 Project Structure

fruits-vegetables-detection/
├── images/                # Static images (hero, backgrounds, etc.)
│   └── hero-bg.jpg
│
├── templates/             # HTML templates
│   ├── index.html         # Home page (upload detection)
│   ├── live.html          # Live webcam detection
│   └── result.html        # Display results
│
├── weights/               # Model weights
│   └── best.pt            # YOLOv5 trained model
│
├── app.py                 # Flask application
├── data.yaml              # Dataset configuration file
├── requirements.txt       # Python dependencies
└── README.md              # Project documentation

📈 Optimizations

Custom YOLOv5 training with 32 classes of fruits/vegetables.

Lightweight Flask API with minimal latency.

Reusable calorie lookup function.

Separate HTML templates for clean UI organization.

🌐 Deployment (Future Scope)

Backend (Flask) → Deploy on Render, Heroku, or AWS EC2.

Frontend Templates → Can be extended with React / modern UI later.

Configure environment variables for production.

🤝 Contributing Contributions are welcome! Fork this repo, create a branch, and submit a PR.

📜 License This project is licensed under the MIT License.

👨‍💻 Author Built with ❤️ by Syed Abdul Qadeer Currently pursuing Full-Stack Web Development @ Masai School #dailylearning #masaiverse

🔖 Tags Flask · PyTorch · YOLOv5 · Computer Vision · Machine Learning · Deep Learning · Object Detection · Fruits Detection · Vegetables Detection · Calorie Tracking · Portfolio Project