Classifying Poses with ml5.js Part 2 | Summary and Q&A

TL;DR

This tutorial demonstrates how to use PoseNet and ML5.js to build a pose recognition model for classifying different poses.

Key Insights

• 💨 PoseNet and ML5.js provide a convenient way to create pose recognition models without extensive coding knowledge.
• 🚂 Collecting a diverse and well-labeled dataset is crucial for training an accurate model.
• 🚂 It's important to normalize the pose data values before training the model for better performance.

Transcript

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Questions & Answers

Q: How can I capture pose data for training the model?

Press a key to set the label, strike the pose, and wait for the collection period to end. Repeat this process for each pose you want to train.

Q: How can I improve the accuracy of the pose recognition model?

You can collect a larger dataset with more varied poses to improve the accuracy. Additionally, you can experiment with different neural network architectures and training options.

Q: Can I use different programming languages to train the model?

Yes, you can train the model using Python and libraries like TensorFlow or Keras before deploying it with ML5.js.

Summary & Key Takeaways

• The tutorial begins with introducing the need for pose recognition and the basics of PoseNet and ML5.js.

• It covers the process of capturing pose data, training a neural network model, and saving the trained model.

• The tutorial also provides guidance on deploying the trained model and integrating it into a sketch for pose recognition.