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--- |
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datasets: |
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- garythung/trashnet |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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--- |
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## Model Overview |
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This model classifies images from the TrashNet dataset into one of six categories: Cardboard, Glass, Metal, Paper, Plastic, and Trash. It uses a convolutional neural network (CNN) architecture for image classification tasks, specifically aimed at waste management and recycling systems. |
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- Dataset: TrashNet (6 waste categories) |
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- Architecture: CNN with 3 convolutional layers, max pooling, and fully connected layers. |
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- Evaluation Metrics: Accuracy, Precision, Recall, F1-Score |
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- Repository: https://github.com/randyver/trash-classification.git |
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## Use Cases |
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- Direct Use: Classify waste images for recycling or waste management systems. |
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- Downstream Use: Can be integrated into smart recycling and waste sorting ecosystems. |
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- Limitations: Not suitable for fine-grained classification or tasks outside of waste classification (e.g., medical, security). |
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## Training Details |
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- Data: Preprocessed TrashNet dataset (images resized and normalized). |
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- Hyperparameters: Learning rate: 0.001, Batch size: 32, Epochs: 10. |
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- Model Architecture: Standard CNN with convolutional layers followed by max pooling and fully connected layers. |
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## Recommendations |
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- Retraining: Retrain the model if expanding to new waste categories or environments. |
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- Image Quality: Ensure high-quality images for optimal performance. |
