Data · AI · Development
Healthy food classifier
Binary healthy food classifier with a custom CNN on Food-101 and a Streamlit demo.
procedural cover — real screenshots pendingProcedural cover for the project Healthy food classifier.
01 Summary
A computer vision system that classifies images of food dishes as healthy or unhealthy. It reorganizes the 101 categories from the Food-101 dataset into two binary classes and trains a CNN built from scratch (FoodNet, three convolutional blocks with BatchNormalization and Dropout) in TensorFlow/Keras. The pipeline covers downloading from Kaggle, cleaning corrupted images, class balancing via data augmentation, training with EarlyStopping, and confusion matrix evaluation, achieving 71% validation accuracy. Includes a Streamlit web interface.
02 Results
Verifiable figures in the repository and its documentation:
0.71accuracy en validación
0.78F1 clase no saludable
22117imágenes de validación
03 Highlights
- Custom 3-block convolutional CNN (FoodNet) with BatchNormalization and Dropout, instead of transfer learning.
- Reproducible 6-script pipeline: Kaggle download, reorganization of Food-101 into binary classes, data cleaning, data augmentation, training, and evaluation.
- Training with EarlyStopping and ModelCheckpoint; evaluation using a classification report and confusion matrix across 22,117 images.
- Streamlit web interface to classify user-uploaded images in real time, featuring a video demo.
- README with a justified architecture comparison (custom CNN vs. MobileNetV2) and an ethical reflection on dataset bias.
04 Screenshots
Screenshot pending
Interface — pending