Learn to implement pose estimation using MoveNet Lightning, the fastest Python deep learning model for fitness applications, in this comprehensive tutorial video. Discover how to install MoveNet for Python, load it using TFLite, render pose estimation results from scratch, and perform real-time pose estimation with OpenCV. Follow along as the instructor guides you through installing dependencies, loading the TFLite model, making pose detections, drawing keypoints, and creating connections. Access the provided GitHub repository for code samples and explore additional resources, including model downloads and documentation. Perfect for developers interested in integrating cutting-edge pose estimation technology into their projects.
Pose Estimation with the Fastest Python Deep Learning Model - MoveNet Lightning
Overview
Syllabus
- Start
- Introduction
- Gameplan
- How it Works
- Tutorial
- 0. Install and Import Dependencies
- 1. Load TFLite Model
- 2. Make Pose Detections
- 3. Draw Keypoints
- 4. Draw Connections
- Wrap Up
Taught by
Nicholas Renotte
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