Waste Classifier AI

Dataset Structure

waste_dataset/
├── biodegradable/
│   ├── img1.jpg
│   └── ...
└── non_biodegradable/
    ├── img2.jpg
    └── ...

Training the Model

1. Install dependencies:
   pip install -r requirements.txt
2. Run the training script:
   python train_waste_classifier.py
   This will save the model and processor to models/waste_classifier_model/.

Running the API

1. Start the Flask API:
   python waste_classification_api.py
2. The API will be available at http://localhost:5000/classify.

API Usage

• Endpoint: /classify (POST)
• Payload: Multipart form with one or more images (field name: images)
• Response:
  json

  1{ 2 "results": [ 3 { 4 "label": "biodegradable", 5 "confidence": 0.94, 6 "description": "Easily breaks down naturally. Good for composting.", 7 "recyclable": false, 8 "disposal": "Use compost or organic bin", 9 "example_items": ["banana peel", "food waste", "paper"] 10 }, 11 ... 12 ] 13}

Frontend Integration

• The React Native frontend can POST images to /classify and display the results using the modal.
• No changes are needed to the modal if it expects the above JSON structure.

Notes

• If your dataset folders are named differently (e.g., R and O), update the LABEL2INFO mapping and class names in the training script.
• The model is based on google/vit-base-patch16-224 and fine-tuned for binary classification.