README.md · randyver/trash-classification-cnn at main

metadata

datasets:
  - garythung/trashnet
language:
  - en
metrics:
  - accuracy
  - f1
  - recall
  - precision

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.