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README.md

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-title: Mae Waste Classifier Demo
-emoji: 😻
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: MAE Waste Classifier
+emoji: 🗂️
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+models:
+- ysfad/mae-waste-classifier
+datasets:
+- garythung/trashnet
+tags:
+- computer-vision
+- image-classification
+- waste-management
+- recycling
+- mae
+- vision-transformer
+- environmental
+---
+
+# 🗂️ MAE Waste Classification System
+
+An intelligent waste classification system using a **finetuned MAE (Masked Autoencoder) ViT-Base model** that achieves **93.27% validation accuracy** on 9 waste categories.
+
+## 🎯 Features
+
+- **High Accuracy**: 93.27% validation accuracy on waste classification
+- **Fast Inference**: Optimized ViT-Base architecture for real-time classification
+- **Comprehensive**: Covers 9 major waste categories
+- **Smart Instructions**: Provides specific disposal instructions for each item
+- **Modern UI**: Clean, intuitive Gradio interface
+
+## 🏆 Model Performance
+
+- **Architecture**: Vision Transformer (ViT-Base) with MAE pretraining
+- **Training Data**: RealWaste dataset (4,752 images)
+- **Validation Accuracy**: 93.27%
+- **Training Accuracy**: 99.89%
+- **Parameters**: 86M parameters
+- **Preprocessing**: MAE-style image preprocessing
+
+## 📊 Waste Categories
+
+The model can classify the following 9 waste categories:
+
+1. **Cardboard** - Recyclable paper-based packaging
+2. **Food Organics** - Compostable food waste
+3. **Glass** - Recyclable glass containers
+4. **Metal** - Recyclable metal items (cans, foil)
+5. **Miscellaneous Trash** - General non-recyclable waste
+6. **Paper** - Recyclable paper products
+7. **Plastic** - Various plastic items
+8. **Textile Trash** - Fabric and clothing waste
+9. **Vegetation** - Organic plant matter
+
+## 🔬 Technical Details
+
+### Model Architecture
+- **Base Model**: Vision Transformer (ViT-Base/16)
+- **Pretraining**: MAE (Masked Autoencoder) self-supervised learning
+- **Finetuning**: Supervised classification on RealWaste dataset
+- **Input Size**: 224x224 pixels
+- **Patch Size**: 16x16 pixels
+
+### Training Process
+1. **Pretraining**: MAE self-supervised learning on ImageNet
+2. **Finetuning**: Classification head training on RealWaste dataset
+3. **Optimization**: AdamW optimizer with learning rate scheduling
+4. **Data Augmentation**: Standard vision transforms
+
+### Performance Metrics
+- **Validation Accuracy**: 93.27%
+- **Training Accuracy**: 99.89%
+- **Training Time**: ~15 epochs
+- **Hardware**: NVIDIA RTX 3080 Ti
+
+## 🚀 Usage
+
+### Online Demo
+Simply upload an image of a waste item to get:
+- **Classification** with confidence score
+- **Disposal instructions** for proper waste management
+- **Top-k predictions** with detailed breakdown
+
+### Model Access
+The trained model is available on Hugging Face Hub:
+- **Model**: [ysfad/mae-waste-classifier](https://huggingface.co/ysfad/mae-waste-classifier)
+- **Format**: PyTorch checkpoint
+- **Size**: ~1GB
+
+### Local Usage
+```python
+from mae_waste_classifier import MAEWasteClassifier
+
+# Load model from HF Hub
+classifier = MAEWasteClassifier(hf_model_id="ysfad/mae-waste-classifier")
+
+# Classify image
+result = classifier.classify_image("path/to/image.jpg")
+print(f"Predicted: {result['predicted_class']} ({result['confidence']:.3f})")
+```
+
+## 🌍 Environmental Impact
+
+This system supports sustainable waste management by:
+- **Reducing contamination** in recycling streams
+- **Educating users** about proper disposal methods
+- **Improving sorting accuracy** in waste facilities
+- **Promoting recycling** awareness
+
+## 📈 Dataset
+
+Trained on the **RealWaste** dataset:
+- **Total Images**: 4,752
+- **Training Split**: 3,801 images (80%)
+- **Validation Split**: 951 images (20%)
+- **Categories**: 9 waste types
+- **Quality**: High-resolution real-world images
+
+## 🔧 Technical Requirements
+
+- **Python**: 3.8+
+- **PyTorch**: 2.0+
+- **Transformers**: Latest
+- **Gradio**: 4.44.0+
+- **PIL**: Image processing
+- **NumPy**: Numerical operations
+
+## 📝 License
+
+This project is licensed under the MIT License. See the model repository for more details.
+
+## 🤝 Contributing
+
+Contributions are welcome! Areas for improvement:
+- Additional waste categories
+- Multi-language support
+- Mobile optimization
+- Integration with IoT devices
+
+## 📞 Contact
+
+For questions or collaboration opportunities, please reach out through the Hugging Face model repository.
+
+---
+
+**🌱 Built for a sustainable future through AI-powered waste management**