MediaPipe-Hand-Gesture-Recognition
Real‑time hand gesture recognition optimized for mobile and edge.
The MediaPipe Gesture Recognizer is a real‑time machine learning pipeline that detects hands, predicts 21 hand landmarks, determines handedness (left/right), and classifies gestures from a predefined set
Not supported
This model is currently not supported on any IoT chipset.
To see performance metrics for this model on other chipsets, click the button below.
View for other chipsetsTechnical Details
Input resolution:256x256
Number of parameters (palm_detector):1.76M
Model size (palm_detector) (w8a8):2.05 MB
Number of parameters (hand_landmark_detector):2.72M
Model size (hand_landmark_detector) (w8a8):3.12 MB
Number of parameters (canned_gesture_classifier):143K
Model size (canned_gesture_classifier) (w8a8):180 KB
Model size (palm_detector) (float):6.75 MB
Model size (hand_landmark_detector) (float):10.4 MB
Model size (canned_gesture_classifier) (float):577 KB
Applicable Scenarios
- Gesture Control
- Virtual Reality
- Gaming
License
Model:APACHE-2.0
Tags
- real-time
Supported IoT Devices
- Dragonwing IQ-9075 EVK
- Dragonwing IQ-X5121
- Dragonwing IQ-X7181
- Dragonwing Q-6690 MTP
- Dragonwing Q-7790
- Dragonwing Q-8750
- Dragonwing RB3 Gen 2 Vision Kit
- QCS8275 (Proxy)
- QCS8550 (Proxy)
Supported IoT Chipsets
- Qualcomm® QCM6690
- Qualcomm® QCS6490
- Qualcomm® QCS8275 (Proxy)
- Qualcomm® QCS8550 (Proxy)
- Qualcomm® QCS9075
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