Deploy a PoseNet Model

This project is part of the Human Pose Estimation with TensorFlow.js and React bundle.
prerequisites
basics of JavaScript • basics of HTML • basics of React • basics of TensorFlow.js • basics of machine learning and web development
skills learned
setting up TensorFlow.js PoseNet model in React Web app • enabling a WebGL backend • setting up Webcam live stream processing
Andrej Baranovskij
1 week · 4-6 hours per week · INTERMEDIATE

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liveProject This project is part of the Human Pose Estimation with TensorFlow.js and React bundle. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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In this liveProject, you’ll set up and tune a TensorFlow.js PoseNet model in a React web app. PoseNet allows real-time human pose estimation in your browser, all through the magic of TensorFlow.js. You’ll enable a WebGL backend, set up a system where PoseNet can utilize a webcam stream for its data input, and establish a canvas that can draw on top of an estimated pose.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Andrej Baranovskij
Andrej Baranovskij is a TensorFlow-certified developer who runs his own machine learning startup company Katana ML. Andrej is responsible for building machine learning products for enterprise operations automation. Previously, he spent 15 years working with Oracle technology and building various enterprise systems across the globe. His software development experience allows to bridge a gap between machine learning and software development.

prerequisites

This liveProject is for both web developers and machine learning engineers. Web developers experienced with JavaScript will learn how to integrate TensorFlow into their applications, while ML engineers will be interested in developing their skills for user interfaces.

TOOLS
  • Basics of JavaScript
  • Basics of HTML
  • Basics of Node.js, NPM, and Yarn
  • Basics of React
  • Basics of TensorFlow.js
  • Visual Studio Code
TECHNIQUES
  • Basics of machine learning
  • Basics of web development with HTML/JavaScript

you will learn

In this liveProject, you’ll learn to use the TensorFlow.js library as part of a React app. You’ll master the basics of machine learning and web development, and how to combine the two.

  • Setting up TensorFlow.js PoseNet model in React Web app
  • Enabling WebGL backend
  • Setting up webcam livestream processing in React Web app
  • Feeding webcam livestream data into PoseNet model for pose estimation
  • Drawing detected pose keypoints and skeleton on the canvas
  • Implementing basic React UI to start and stop pose estimation

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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