🍕 Food Detection AI Application

A comprehensive food detection application using YOLOv8 for detecting 10 different food classes in images.

🎯 Supported Food Classes

1. Apple Pie 🥧
2. Chocolate 🍫
3. French Fries 🍟
4. Hotdog 🌭
5. Nachos 🌮
6. Pizza 🍕
7. Onion Rings 🧅
8. Pancakes 🥞
9. Spring Rolls 🥢
10. Tacos 🌮

🚀 Features

• Web Interface: Beautiful, modern web UI with drag-and-drop functionality
• Command Line Tool: Fast batch processing for multiple images
• Real-time Detection: Instant results with confidence scores
• Bounding Box Visualization: Annotated images with detection boxes
• Batch Processing: Process entire directories of images
• API Endpoint: RESTful API for integration with other applications

📋 Requirements

• Python 3.8+
• YOLOv8 model (already trained and available in runs/detect/yolov8n_food101/weights/best.pt)

🛠️ Installation

1. Install Dependencies:

   pip install -r requirements.txt

2. Verify Model Path: Ensure your YOLO model is located at:

   runs/detect/yolov8n_food101/weights/best.pt
   

🎮 Usage

1. Web Application

Start the Flask server:

python food_detection_app.py

Access the application:

• Open your browser and go to: http://localhost:5000
• Upload images by dragging and dropping or clicking to browse
• View real-time detection results with bounding boxes

2. Command Line Tool

Single Image Detection:

python food_detection_cli.py path/to/image.jpg -o output.jpg

Batch Processing:

python food_detection_cli.py path/to/images/folder -d output/folder

List Supported Classes:

python food_detection_cli.py --list-classes

Adjust Confidence Threshold:

python food_detection_cli.py image.jpg -c 0.7

3. API Usage

Upload and Detect:

curl -X POST -F "[email protected]" http://localhost:5000/upload

API Response Format:

{
  "success": true,
  "detections": [
    {
      "class_name": "Pizza",
      "confidence": 95.2,
      "bbox": [100, 150, 300, 400]
    }
  ],
  "total_detections": 1,
  "original_image": "timestamp_image.jpg",
  "annotated_image": "annotated_timestamp_image.jpg"
}

📁 File Structure

├── food_detection_app.py          # Flask web application
├── food_detection_cli.py          # Command-line tool
├── requirements.txt               # Python dependencies
├── templates/
│   └── index.html                # Web interface template
├── uploads/                      # Temporary uploaded files
├── results/                      # Annotated output images
└── runs/detect/yolov8n_food101/weights/
    └── best.pt                   # Trained YOLO model

🎨 Web Interface Features

• Modern UI: Beautiful gradient design with smooth animations
• Drag & Drop: Easy image upload with visual feedback
• Real-time Results: Instant detection with loading indicators
• Statistics: Total detections and unique food types
• Responsive Design: Works on desktop and mobile devices
• Error Handling: Clear error messages for troubleshooting

🔧 Configuration

Model Settings

• Model Path: runs/detect/yolov8n_food101/weights/best.pt
• Confidence Threshold: Default 0.5 (50%)
• Max File Size: 16MB for web uploads

Web Server Settings

• Host: 0.0.0.0 (accessible from any IP)
• Port: 5000
• Debug Mode: Enabled for development

📊 Performance

• Inference Speed: ~50-100ms per image (depending on hardware)
• Accuracy: Trained on Food-101 dataset subset
• Memory Usage: ~2-4GB RAM (depending on image size)

🐛 Troubleshooting

Common Issues

1. Model Not Found:

   ❌ Error loading YOLO model: [Errno 2] No such file or directory
   

   Solution: Ensure the model file exists at the specified path

2. CUDA/GPU Issues:

   ❌ CUDA out of memory
   

   Solution: Use CPU mode or reduce batch size

3. Import Errors:

   ModuleNotFoundError: No module named 'ultralytics'
   

   Solution: Install requirements: pip install -r requirements.txt

Performance Tips

• GPU Acceleration: Install CUDA-enabled PyTorch for faster inference
• Batch Processing: Use CLI tool for processing multiple images
• Image Optimization: Resize large images before processing

🔄 API Endpoints

Endpoint	Method	Description
/	GET	Main web interface
/upload	POST	Upload and detect food in image
/detect	POST	API endpoint for detection
/uploads/<filename>	GET	Serve uploaded files
/results/<filename>	GET	Serve result files

📈 Future Enhancements

• Real-time video detection
• Mobile app integration
• Nutritional information lookup
• Recipe suggestions based on detected foods
• Multi-language support
• Cloud deployment options

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

This project is part of the AIML Computer Vision Food-101 Detection project.

🙏 Acknowledgments

• YOLOv8 by Ultralytics
• Food-101 dataset
• Flask web framework
• OpenCV for image processing

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Happy Food Detection! 🍕🍔🍟