File size: 1,678 Bytes
87bd492 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # 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:**
```json
{
"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. |