Claudineuwa/Waste_Classifier

Waste Classifier AI

Dataset Structure

Place your dataset in a directory like:

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:

  {
    "results": [
      {
        "label": "biodegradable",
        "confidence": 0.94,
        "description": "Easily breaks down naturally. Good for composting.",
        "recyclable": false,
        "disposal": "Use compost or organic bin",
        "example_items": ["banana peel", "food waste", "paper"]
      },
      ...
    ]
  }
  

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.