Update README.md

README.md

@@ -8,4 +8,37 @@ metrics:
 - f1
 - recall
 - precision
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+---
+
+## 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).
+
+## Performance
+
+- Validation Accuracy: 69.31%
+- Precision: 70%
+- Recall: 69%
+- F1-Score: 70%
+
+## 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.