Food Classification with ResNet and EfficientNet

This project explores the application of two powerful convolutional neural network architectures, ResNet and EfficientNet, for the task of food classification. The goal is to compare their performance in accurately categorizing various types of food images, enabling better understanding and usage of these models in practical AI applications related to culinary arts and health.

Model Implementation: Implement ResNet and EfficientNet models using TensorFlow and Keras to classify food images.

Performance Comparison: Evaluate and compare the accuracy, speed, and computational efficiency of each model on a standardized food image dataset.

Optimization: Fine-tune hyperparameters and apply techniques like data augmentation to enhance model performance.

Analysis: Provide insights into the models’ classification layers and decision-making processes using visualization tools.

TensorFlow

Keras

Python

Matplotlib for visualizations

This project aims to provide a detailed comparative analysis of ResNet and EfficientNet models, offering valuable insights for developers and researchers interested in applying these architectures in the food domain. The repository includes all source code, a set of pretrained models, and a Jupyter notebook that guides users through the process of training, evaluating, and understanding the models.