SnapChef Edibility Classifier
This model classifies loose food images into two categories:
Model Architecture
The model uses EfficientNet-B0 with transfer learning
The original pretrained layers were frozen during the first training stage
The final 20 EfficientNet-B0 layers were later fine tuned using a lower learning rate
Optimisation
The model was improved using:
- Data augmentation
- Dropout
- Early stopping
- Learning rate reduction
- Fine tuning
- Hard example retraining
Hard example retraining was used after a fresh red apple was incorrectly classified as inedible during real world testing
Performance
- Final validation accuracy: 96 percent
- Final validation loss: 0.1364
- Test accuracy before hard example retraining: 93.33 percent
Labels
Input
The model accepts RGB food images resized to 224 by 224 pixels
Intended Use
This model is designed for the SnapChef food waste reduction application
It helps users estimate whether loose food appears edible or inedible based on an uploaded image
Limitations
The model may make incorrect predictions for food types or visual conditions that were not sufficiently represented in the training dataset
Predictions should not replace proper food safety inspection or professional advice
