https://github.com/npatta01/mobile-deep-learning-classifier

Tutorial on building and deploying a Mobile Deep Learning Classifier for food
https://github.com/npatta01/mobile-deep-learning-classifier

cross-platform deep-learning expo fastai food-classification mobile mobile-app pytorch react-native

Last synced: 13 days ago
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Tutorial on building and deploying a Mobile Deep Learning Classifier for food

• Host: GitHub
• URL: https://github.com/npatta01/mobile-deep-learning-classifier
• Owner: npatta01
• License: mit
• Created: 2018-11-06T21:01:16.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T17:38:42.000Z (almost 2 years ago)
• Last Synced: 2024-10-23T21:58:01.005Z (22 days ago)
• Topics: cross-platform, deep-learning, expo, fastai, food-classification, mobile, mobile-app, pytorch, react-native
• Language: TypeScript
• Homepage: https://food-img-classifier.herokuapp.com/
• Size: 14 MB
• Stars: 65
• Watchers: 4
• Forks: 28
• Open Issues: 19
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Creating a Mobile App 

This repo contains a template for building a deep learning mobile classifier.

## Citation Note

If you do use our blog or GitHub repos to create your own web or mobile app, we would appreciate it if you would give our work attribution by sharing the below citation:  

>Pattaniyil, Nidhin and Shaikh, Reshama, [Deploying Deep Learning Models On Web And Mobile](https://reshamas.github.io/deploying-deep-learning-models-on-web-and-mobile/), 2019

Here is a link to a demo of our mobile app on [YouTube](https://www.youtube.com/watch?v=7d2qFLeYvRc&t=1s)

![Demo](docs/images/demo.gif)

## Tools

The following were used for mobile model **deployment**:    

1. Expo (React Native)

2. XCode (optional)

3. Web app deployed on heroku 

## Assumptions

- a deep learning model served as a web app that responds to 

/api/classify

/api/classes

## Setup 

Detailed Walkthrough [Link](docs/2_expo_app.md)

## Author

This project was completed jointly by [Nidhin Pattaniyil](https://www.linkedin.com/in/nidhinpattaniyil/) and [Reshama Shaikh](https://reshamas.github.io).

Feel free to reach out with questions/suggestions.