| --- |
| 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. |
