Hey what's up, y'all! In this video we'll take a look at a really cool GitHub repo that I found that allows us to easily train a Keras neural network to recognize our own custom hand gestures, and that runs flawlessly on a CPU. But there's more: I'll actually guide you through the entire process and explain the logic of how everything works behind the scenes too. We'll use Mediapipe for extracting hand landmarks (which runs a pipeline of TensorFlow neural nets under the hood), which will allow us to use relatively small training datasets for training on our new custom hand gestures.

GitHub repository (translated into English): [ Ссылка ]
Original GitHub repository (in Japanese) [ Ссылка ]
A series of videos I made about OpenCV: [ Ссылка ]
Here's the same repo but in React, and nextJS: [ Ссылка ]

Follow me on GitHub: [ Ссылка ]
Hit me up on Twitter: [ Ссылка ] ( Cool updates on more videos I'm making there)
And on LinkedIn: [ Ссылка ]

Time stamps⏳
00:00 Intro
01:14 What's gonna be in the video
04:00 Top-level overview of the hand gesture recognition approach we'll use
14:56 Google's Mediapipe framework Python API
17:50 Hand gesture recognition GitHub repository we'll use
20:58 Modifying the repo's code to allow for multi-hand detection
25:23 Code walkthrough
32:46 Explaining hand landmarks preprocessing algorithm
48:05 Disabling point history classification
50:43 Training: hand gesture dataset
53:30 Training: adding a new hand gesture to the existing ones
01:02:43 Training: Retraining the model with all new hand gestures
01:09:51 Outro

Contact me directly: ivangoncharovai@gmail.com
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