/Food-Image-Recognition

A system that takes food images as an input, recognizes the food automatically and gives the nutritional-facts as an output.

Primary LanguageJupyter NotebookMIT LicenseMIT

Food-Image-Recognition

Contributors Stargazers Followers MIT License Open Source? Yes!

Table of Contents

About the Project

Food

Photo by Jay Wennington on Unsplash

Overview

  • Each year, approximately 6,78,000 deaths are caused in the United States of America due to unhealthy diet.
  • A typical American diet is too high in calories, fat, sugars, sodium, etc.
  • Hence, people have became more proactive when it comes to health matters.
  • Services like eating habit recorder and calorie/nutrition calculator have became extremely popular.
  • They can make users aware of problems like obesity, cancer, diabetes, heart-disease, etc. that can be caused by unhealthy diets.
  • Most of these services require the users to manually select a food item from a hierarchical menu which is a time consuming process and not so user friendly.
  • An user-interactive system that takes food images as an input, recognizes the food automatically and gives the nutritional-facts as an output will save a lot of time.
  • This system can be used in various areas such as social network, health-care applications, eating-habit evaluations, etc.
  • For food image recognition we will be using transfer learning to retrain the final layer (with 101 additional food-classes) of Inception-v3 model which is already trained by Google on 1000 classes.
  • It almost took 10-11 hours to train the model on Google Colab.

Built With

Dataset

Food Images Source: The Food-101 Data Set

  • The data set consists of 101 food categories, with 1,01, 000 images.
  • 250 test images/per class and 750 training images/per class are provided.
  • All the images were rescaled to have a maximum side length of 512 pixels.

Nutrition Information Source: Food Data Central API

  • U.S. Department of Agriculture, Agricultural Research Service. FoodData Central, 2019. fdc.nal.usda.gov.

Results

Demo

demo

Accuracy

Accuracy

Loss

Loss

Testing on random images.

Test

Visualization of different layers.

Layers

Heat-Map & Class-Activation-Map

Heatmap

Contributing

Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/amazing-feature)
  3. Commit your Changes (git commit -m 'feat: some amazing feature')
  4. Push to the Branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Linkedin

Maharsh Suryawala - Portfolio

Project Link: https://github.com/MaharshSuryawala/Food-Image-Recognition

References

Acknowledgements

forthebadge

ForTheBadge built-with-love