Food-Image-Recognition

Table of Contents

Food-Image-Recognition

About the Project

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

Accuracy

Loss

Testing on random images.

Visualization of different layers.

Heat-Map & Class-Activation-Map

Contributing

Any contributions you make are greatly appreciated.

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

License

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

Contact

Maharsh Suryawala - Portfolio

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

References

Acknowledgements