README.md

metadata

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
• Format: PyTorch checkpoint
• Size: ~1GB

Local Usage

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.

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🌱 Built for a sustainable future through AI-powered waste management