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### Architecture Model
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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,
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Dropout layer (0.3) for regularization, and Output layer with 6 units (one per waste category).
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### Model Performance
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The model achieves strong performance across all waste categories, with an overall accuracy of 96%. Detailed performance metrics per class:
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### Architecture Model
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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,
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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
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The model achieves strong performance across all waste categories, with an overall accuracy of 96%. Detailed performance metrics per class:
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