Princess Finder

I've borrowed all the Disney Princess dolls from my daughter to build a Machine Learning model such that, an application can recognize them with confidence using a webcam. I have given it a name too. The app is called, Princess Finder.

Technologies Used

The Princess Finder app is built using,

• The Teachable Machine: How about an easy and fast way to create machine learning models that you can directly use in your app or site? The Teachable Machine allows you to train a computer with images, sounds, and poses. We have created a model using the Disney princess so that, we can perform an Image Classification by using it in our app.
• ml5.js: It is machine learning for the web using your web browser. It uses the web browser's built-in graphics processing unit (GPU) to perform fast calculations. We can use the API like, imageClassifier(model), classify, etc. to perform the image classification.
• React: It is a JavaScript library for building user interfaces. We can use ml5.js in a React application just by installing and importing the dependency.

Here is a snap from the app shows, it is 93% confident that the princess is Jasmine. It also marks it with a golden ring.

Whereas, there is no way I look like a Disney Princess(not even a doll). Hence my own image has been classified correctly saying, No Dolls.

Want to Motivate?

Thanks for your time to reading this. Feel free to clone/fork/improve. Who doesn't want motivations? Give the project a star(⭐) if you liked the work.

Many Thanks to all the Stargazers who has supported this project with stars(⭐)

Demo

You can find a Live Demo from here

Build Status:

How to Run this Project Locally?

This project was bootstrapped with Create React App.

In the project directory, you can run:

# Or npm install

yarn install

Then,

# Or npm run start

yarn start

This will run the app in the development mode.
Open http://localhost:3000 to view it in the browser.

The page will reload if you make edits. You will also see any lint errors in the console.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Michael Currin 🐛

Tapas Adhikary 💻

This project follows the all-contributors specification. Contributions of any kind welcome!

License

Released under MIT by @atapas.