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 totrain
a computer with images, sounds, and poses. We have created a model using the Disney princess so that, we can perform anImage 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(
Stargazers
who has supported this project with stars(⭐ )
Many Thanks to all the
Demo
You can find a Live Demo from here
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!