https://github.com/vidursatija/tf-hack2.0

⚡#PoweredByTF 2.0 Challenge! [ /w @inishchith ]
https://github.com/vidursatija/tf-hack2.0

cv green-app hackathon javascript machine-learning python python3 tensorflow

Last synced: 2 days ago
JSON representation

⚡#PoweredByTF 2.0 Challenge! [ /w @inishchith ]

• Host: GitHub
• URL: https://github.com/vidursatija/tf-hack2.0
• Owner: vidursatija
• License: mit
• Created: 2018-09-02T08:27:57.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-11-21T21:54:14.000Z (about 2 years ago)
• Last Synced: 2024-12-06T21:29:32.517Z (about 2 months ago)
• Topics: cv, green-app, hackathon, javascript, machine-learning, python, python3, tensorflow
• Language: Python
• Homepage: https://tensorflow.devpost.com/
• Size: 6.88 MB
• Stars: 18
• Watchers: 3
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TensorFlow 2.0 Hackathon

⚡ #PoweredByTF 2.0 Challenge! [ Devpost ]



    

        

            live demo

         

    

    
 

    

    
  MoboView 







## Story

A lot of countries have started segregating garbage into recyclable and non recyclable. But many times, a lot of people won't be familiar with the items that can be recycled or not. It is beneficial to remember such information but not everyone can do that. To help that, we present a web application which tells you if an object is recyclable or not. But what is special about this? You don't have to put in name of things and search for items. You just click a photo of the object and the app will tell you which garbage bin to throw it into.

## Development

The front end is made is javascript. This then calls a flask app running in the backend which does preprocessing. The flask app then calls the served model. The architecture used is VGG16. Although it's very outdated but it gives a nice beginning to people who are new to Tensorflow-2.0.

PS: Do checkout our video and website. Star us on GitHub and let us know if you have questions/ideas to share!

### How to run

#### Model

See the model/ dir for the README

#### App

Run the app using 

```python3 app.py```

Make sure to set the correct ports and IP addresses for the tensorflow served model and flask app

#### Web

This can be run directly. In app.js make sure the api calls the flask app IP address