Objective
Explore and document the logic and underlying models used by MediaPipe HandsLandmark, including:
- How MediaPipe detects and tracks hand landmarks
- The structure of the model and inference pipeline
Areas to investigate
- Code examples for running HandsLandmark in MediaPipe
- Explanation of how keypoints are calculated and returned
- Typical use cases and limitations for model use
Expected outcome
A concise summary of the detection logic, the model architecture, and example usage for MediaPipe HandsLandmark, enabling better understanding and practical application.
Objective
Explore and document the logic and underlying models used by MediaPipe HandsLandmark, including:
Areas to investigate
Expected outcome
A concise summary of the detection logic, the model architecture, and example usage for MediaPipe HandsLandmark, enabling better understanding and practical application.