Food-Image-Recognition
Table of Contents
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
- https://cspinet.org/eating-healthy/why-good-nutrition-important
- https://www.tensorflow.org/api_docs/python/tf/keras/applications/InceptionV3
Acknowledgements
