msalehi01 / food-image-classification

Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.

Food-Image-Classification

Task instruction below

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GitHub Description:

🍔 Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.

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README.md

Food Image Classification using TensorFlow

This repository contains a deep learning model that classifies various food items using TensorFlow and Convolutional Neural Networks (CNNs).

Project Overview

The goal of this project is to build a model that can accurately classify images of food into predefined categories. With the rise of health and fitness apps, such a model can be integrated into applications to automatically detect and log consumed food items based on user-uploaded images.

Dataset

The dataset used for this project consists of images of various food items categorized into different classes. Each image is labeled with its corresponding food category. Available here

Features

• Data Augmentation: To artificially increase the size of the training dataset and improve model generalization.
• Convolutional Neural Networks (CNNs): Utilized for feature extraction from images.
• Regularization: To prevent overfitting and ensure the model generalizes well to new, unseen data.
• Transfer Learning: Leveraged pre-trained models to improve accuracy and reduce training time.

Requirements

• TensorFlow 2.x
• Python 3.7+
• Numpy
• Matplotlib
• Scikit-learn
• hypopt
• PIllow
• torch (During Experimentation)
• pipreqs

Usage

1. Clone the repository:

git clone https://github.com/your_username/food-image-classification.git

2. Navigate to the project directory and install the required packages:

cd food-image-classification

Use  pipreqs to obtain requirements

3. Run the main script in the orderr represented in the AI Algorithm .ipynb here on google colab to train the model: `
4. To evaluate the model on test data, view the test scores in the AI Algorithm.ipyb file:

Results

The model achieved a test accuracy of 92% on the test dataset. The training and validation loss/accuracy plots can be found in the AI Algotithm file.

Future Work

• Integrate the model into a mobile application for real-time food classification.
• Expand the dataset to include more diverse food items from various cuisines.
• Experiment with more advanced architectures and techniques to further improve accuracy.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Special thanks to the creators of the food dataset.
• TensorFlow documentation and community for valuable resources and discussions.

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