For those trying to eat less unhealthy food
This project tackles the challenge of food image classification for promoting healthy eating habits. It implements a Convolutional Neural Network (CNN) to distinguish between healthy and unhealthy food images.
- Dataset:
- A curated dataset of food images categorized as healthy and unhealthy.
- Training and validation folders further divided into healthy and unhealthy subfolders.
- A separate test folder containing images for model evaluation.
- Model:
- A CNN architecture is implemented for image classification.
- Achieved an accuracy of 75% on the validation set.
Overall, this project demonstrates the potential of CNNs in classifying food images for dietary awareness. The provided code and dataset can serve as a foundation for further exploration and improvement.