Model Overview

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.

Use Cases

  • Direct Use: Classify waste images for recycling or waste management systems.
  • Downstream Use: Can be integrated into smart recycling and waste sorting ecosystems.
  • Limitations: Not suitable for fine-grained classification or tasks outside of waste classification (e.g., medical, security).

Training Details

  • Data: Preprocessed TrashNet dataset (images resized and normalized).
  • Hyperparameters: Learning rate: 0.001, Batch size: 32, Epochs: 10.
  • Model Architecture: Standard CNN with convolutional layers followed by max pooling and fully connected layers.

Recommendations

  • Retraining: Retrain the model if expanding to new waste categories or environments.
  • Image Quality: Ensure high-quality images for optimal performance.
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Dataset used to train randyver/trash-classification-cnn