ml5.js: KNN Classification Part 3

TL;DR

Training a KNN model using head gestures for control, saving the model for reuse, and implementing classification in p5.

Transcript

[BELL RINGS] Welcome back. I'm going to finish this ml5 KNN classification example for like a gesture-based controller. So I just-- in between the last video and this one, I just sort of trained a quick KNN model where if my head is up here, it registers as up. If my head is down here, it registers as down. Over here would be to the left, and over ... Read More

Key Insights

• 🛟 MobileNet serves as an essential image classification model within the tutorial.
• 🤟 Training a KNN model involves mapping specific gestures to corresponding labels.
• 👻 Saving the trained model allows for future reuse and prevents data loss.
• 😑 The tutorial highlights the process of loading and utilizing pre-trained models for classification.
• 🪩 CSS transforms are employed to mirror images and provide the desired visual effect.
• 🐛 Bug fixes and workarounds are part of the tutorial to address technical challenges.
• 🖐️ Data collection and training play a crucial role in refining the KNN model's accuracy.

Questions & Answers

Q: How does the KNN model differentiate head gestures for control?

The KNN model registers different gestures based on the head's position - up, down, left, and right by mapping them to specific labels through training.

Q: Why is saving the trained model crucial?

Saving the trained KNN model in JSON format preserves all the learned data, ensuring that it can be reused without having to retrain it constantly.

Q: How does the protagonist handle the bug encountered during the tutorial?

After encountering a bug with saving large JSON files, the creator addressed it by limiting the number of training images until a newer version of ml5 resolved the issue.

Q: What role does MobileNet play in the classification process?

MobileNet provides image features for classification, with its logits being used to build a database that the KNN model utilizes to classify new images based on proximity.

Summary & Key Takeaways

• Demonstrates training a KNN model using head gestures for control.

• Illustrates the importance of saving the model for future use.

• Implements classification using the KNN model in the p5.js environment.