Skip to content

An application for image classification which brings state-of-the-art machine learning algorithms to the public. This is accomplished using tensorflowjs and transfer learning.

Notifications You must be signed in to change notification settings

roop-pal/browser-transfer-learning

Repository files navigation

Authors

Robby Costales (rsc2156)

Vivek Kumar (vk2425)

Yung Pal (roop)

Presentation Link

https://youtu.be/oXccZQUdIDo

Browser Based Transfer Learning

An application for image classification which brings state-of-the-art machine learning algorithms to the public. This is accomplished using tensorflowjs and transfer learning.

Running

To run this website locally, run in the directory containing index.html:

python -m http.server

Observe the hosted location and port, often localhost:8000, and navigate to this in a mobile browser. Alternatively, you can access the hosted website at http://accessible-transfer-learning.s3-website-us-east-1.amazonaws.com/

Evaluation Metrics

In terms of our project, we first evaluated our model based on actual data on a standard dataset like the Kaggle Flowers Dataset. Given MobileNet's high performance, we were unsurprised with high accuracies in the 90s.

What's more important is the performance on the job. As seen in our presentation, we tried taking photos with our phones and evaluating the classifier in our browsers! We found we could classify objects around us, even people!

About

An application for image classification which brings state-of-the-art machine learning algorithms to the public. This is accomplished using tensorflowjs and transfer learning.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published