| ### 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) |
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| ### 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 | |
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