File size: 3,874 Bytes
a8687fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
# Food-Calorie-Estimation with Integrated Generative AI
<!-- TABLE OF CONTENTS -->
## Table of Contents
- [Food-Image-Recognition](#food-image-recognition)
- [Table of Contents](#table-of-contents)
- [About the Project](#about-the-project)
- [Overview](#overview)
- [Built With](#built-with)
- [Dataset](#dataset)
- [Results](#results)
- [Demo](#demo)
- [Visualization of different layers.](#visualization-of-different-layers)
- [Contact](#contact)
- [References](#references)
## About the Project
### 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
* [Python](https://www.python.org/)
* [Jupyter Notebook](https://jupyter.org/)
* [Google Colab](https://colab.research.google.com/)
* [Generative AI](https://pypi.org/project/google-generativeai/)
### Dataset
Food Images Source: [The Food-101 Data Set](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/)
* 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](https://fdc.nal.usda.gov/api-guide.html#bkmk-3)
* U.S. Department of Agriculture, Agricultural Research Service. FoodData Central, 2019. fdc.nal.usda.gov.
## Results
### Demo
### Visualization of different layers.
## Contact
[](https://www.linkedin.com/in/nikhil-chakravarthy-064504203/)
Nikhil Chakravarthy - [Portfolio](https://nikhilchakravarthy.netlify.app)
## References
* [https://cspinet.org/eating-healthy/why-good-nutrition-important](https://cspinet.org/eating-healthy/why-good-nutrition-important)
* [https://www.tensorflow.org/api_docs/python/tf/keras/applications/InceptionV3](https://www.tensorflow.org/api_docs/python/tf/keras/applications/InceptionV3)
<!-- MARKDOWN LINKS -->
<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->
[contributors-shield]: https://img.shields.io/github/contributors/MaharshSuryawala/Food-Image-Recognition?style=flat-square
[contributors-url]: https://github.com/MaharshSuryawala/Food-Image-Recognition/graphs/contributors
[license-shield]: https://img.shields.io/github/license/MaharshSuryawala/Food-Image-Recognition?style=flat-square?style=flat-square
[license-url]: https://github.com/MaharshSuryawala/Food-Image-Recognition?style=flat-square/blob/master/LICENSE.txt
|