---
library_name: keras
pipeline_tag: image-classification
tags:
- tensorflow
- keras
- efficientnet
- food-waste
- image-classification
---

# SnapChef Edibility Classifier

This model classifies loose food images into two categories:

- Edible
- Inedible

## 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

- 0 = Edible
- 1 = Inedible

## 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