This repository explores the possibility to run Machine Learning models on the browser, even on low-power mobile devices. We run an Image Classification task using ReactJs as front-end along with TensorflowJS (with the pretrained MobileNet model) for inference.

MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.

MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

The application is hosted on Github-pages and can be accessed here.

• ReactJS
• TensorflowJS
• MobileNet

All dependencies are enlisted in the package.json

In the project directory, you can run:

This runs 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.

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!