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.
- Dataset: TrashNet (6 waste categories)
- Architecture: CNN with 3 convolutional layers, max pooling, and fully connected layers.
- Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
- Repository: https://github.com/randyver/trash-classification.git
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.
