Object-Detection-with-TFJS

Live Demo

A Live demo can be found at santhtadi.github.io

Introduction

This project show cases the use of Tensorflow JS, a JavaScript library released by Tensorflow, for Object Detection.

Advantages

Tfjs addressed the most common problem with deployment of DL models - Setting up the environment.

With tfjs the model outputs can be shown right in the browser, making it available and easier to use for a larger demographic.

Leveraging the tfjs script we can run inferences on the client-side with virtually no setup.

Disadvantages

Inconsistent User Experience (fps, internet speed) can become a problem for systems with various configurations, but that's the case with all websites and browser apps.

Steps to use this Repo with Custom Object Detection Model

1. Train a SSD MobileNet model from the references given below.
2. Freeze the model.
3. Convert to tfjs format (json and bin files).
4. Use a cloud service to provide the json and model files like IBM or AWS (I used the one hosted by google at https://storage.googleapis.com/tfjs-models/savedmodel/ssdlite_mobilenet_v2/model.json).
5. Edit the model path and label maps in coco-ssd.js
6. Use the script.js to run inference!

References

The tutorial for generating a SSD MobileNet model

The tutorial for inferencing in browser Google codeLabs