| --- |
| title: Real-time Smart Waste Classification |
| emoji: ♻️ |
| colorFrom: green |
| colorTo: blue |
| sdk: gradio |
| sdk_version: 5.45.0 |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: This model can detect different types of wastes in Real-Time |
| --- |
| |
| # ♻️ Smart Waste Classification |
|
|
| An AI-powered web application that classifies different types of waste materials to help with proper recycling and disposal. |
|
|
| ## Features |
|
|
| - **Image Classification**: Upload images of waste items for automatic classification |
| - **10 Categories**: Supports classification of biological waste, glass (brown/green/white), cardboard, metal, paper, plastic, shoes, and general trash |
| - **Recycling Guidance**: Provides clear guidance on whether items are recyclable or not |
| - **Statistics Dashboard**: Track classification statistics with visual charts |
| - **Export Reports**: Download classification data as CSV or PDF reports |
|
|
| ## Supported Waste Categories |
|
|
| ### Recyclable ♻️ |
| - Brown Glass |
| - Green Glass |
| - White Glass |
| - Metal |
| - Plastic |
| - Paper |
| - Cardboard |
|
|
| ### Non-Recyclable 🗑️ |
| - Biological Waste |
| - Shoes |
| - General Trash |
|
|
| ## How to Use |
|
|
| 1. **Upload Image**: Click "Choose File" and select an image of waste material |
| 2. **Classify**: Click "Classify Waste" to get AI prediction |
| 3. **View Result**: See the predicted category, confidence score, and recycling guidance |
| 4. **Check Stats**: Visit the statistics page to see classification trends |
| 5. **Export Data**: Download your classification history as CSV or PDF |
|
|
| ## Technology Stack |
|
|
| - **Backend**: Flask (Python web framework) |
| - **AI Model**: EfficientNet-B0 trained on waste classification dataset |
| - **Frontend**: Bootstrap 5 for responsive UI |
| - **Data Visualization**: Matplotlib for statistics charts |
| - **Export**: CSV and PDF report generation |
|
|
| ## Model Information |
|
|
| The application uses a fine-tuned EfficientNet-B0 model trained specifically for waste classification. The model achieves high accuracy across all supported categories and provides confidence scores for each prediction. |
|
|
| ## Limitations |
|
|
| - Camera functionality is not available in Hugging Face Spaces |
| - Maximum file size limits may apply |
| - Supported image formats: PNG, JPG, JPEG, GIF, BMP |
|
|
| ## License |
|
|
| This project is licensed under the MIT License. |
|
|
| ## Acknowledgments |
|
|
| - Built with TensorFlow/Keras |
| - UI components from Bootstrap |
| - Background images from Unsplash |
