A Beginner's Guide to Machine Learning: PoseNet Tutorial

✨PoseNet is a pre-trained machine learning model that can track your body in real-time using the ml5.js library.

📷PoseNet takes an image as input and outputs an array of coordinates representing key points on the body.

🎯The XY coordinates represent key points on a pose skeleton, which is an arbitrary set of 17 points from the nose to the ankles.

❓PoseNet is trained on the COCO dataset, which contains labeled images of people in various poses.

👨‍💻You can use the ml5.js library and the p5.js library to easily implement PoseNet in interactive media projects.

What are the inputs and outputs of PoseNet?

—PoseNet takes an image as input and outputs an array of coordinates representing key points on the body.

What is the PoseNet skeleton?

—The PoseNet skeleton is an arbitrary set of 17 key points on the body, ranging from the nose to the ankles.

What dataset is PoseNet trained on?

—PoseNet is trained on the COCO dataset, which contains labeled images of people in various poses.

Can I implement PoseNet in my own projects?

—Yes, you can use the ml5.js library and the p5.js library to easily implement PoseNet in interactive media projects.

Where can I find more information about PoseNet?

—For more information about PoseNet, you can refer to the PoseNet GitHub repository and read the blog post on real-time human pose estimation with TensorFlow.js.

00:00Introduction to PoseNet and its use with the ml5.js and p5.js libraries.

02:30Explanation of the inputs and outputs of PoseNet.

03:30Overview of the key points on the body that PoseNet detects.

05:20Discussion of the COCO dataset on which PoseNet is trained.

07:30Tutorial on implementing PoseNet in interactive media projects using ml5.js and p5.js.