### Architecture Model
The model is based on ResNet18 as a backbone and fine-tuning only the last convolutional block (layer4). It also use custom classifier for Hidden layer of 256 units with ReLU activation, 
Dropout layer (0.3) for regularization, and Output layer with 6 units (one per waste category). The dataset is from [garythung/trashnet](https://huggingface.co/datasets/garythung/trashnet)

### Model Performance
The model achieves strong performance across all waste categories, with an overall accuracy of 96%. Detailed performance metrics per class:
| Category   | Precision | Recall | F1-Score | Support |
|------------|-----------|--------|----------|---------|
| Cardboard  | 0.99      | 0.95   | 0.97     | 161     |
| Glass      | 0.92      | 0.98   | 0.95     | 200     |
| Metal      | 0.97      | 0.93   | 0.95     | 164     |
| Paper      | 0.97      | 0.96   | 0.96     | 238     |
| Plastic    | 0.96      | 0.95   | 0.96     | 193     |
| Trash      | 0.90      | 0.96   | 0.93     | 55      |