MediaPipe Hand Detection
MediaPipe Hand Detection is a real-time hand detection model that's optimized for mobile and edge deployment. It can predict bounding boxes and pose skeletons of hands in an image, making it suitable for applications like gesture recognition, virtual try-on, and augmented reality experiences. With an inference time of 2.277 ms on the Samsung Galaxy S23 Ultra, this model is designed to provide fast and accurate results. But how does it achieve this level of performance? The model's architecture and optimization for mobile devices enable it to process images quickly and efficiently. However, its performance may vary depending on the device and runtime used. Despite this, MediaPipe Hand Detection has the potential to revolutionize various industries with its real-time hand detection capabilities.
Deploy Model in Dataloop Pipelines
MediaPipe Hand Detection fits right into a Dataloop Console pipeline, making it easy to process and manage data at scale. It runs smoothly as part of a larger workflow, handling tasks like annotation, filtering, and deployment without extra hassle. Whether it's a single step or a full pipeline, it connects with other nodes easily, keeping everything running without slowdowns or manual work.
Table of Contents
Model Overview
The MediaPipe-Hand-Detection model is a real-time hand detection model optimized for mobile and edge devices. It’s a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.
Key Attributes:
- Model Type: Object detection
- Input Resolution:
256x256 - Number of Parameters:
- MediaPipeHandDetector:
1.76M - MediaPipeHandLandmarkDetector:
2.01M
- MediaPipeHandDetector:
- Model Size:
- MediaPipeHandDetector:
6.76 MB - MediaPipeHandLandmarkDetector:
7.71 MB
- MediaPipeHandDetector:
Functionalities:
- Real-time hand detection
- Predicts bounding boxes and pose skeletons of hands in an image
- Optimized for mobile and edge devices
Example Use Cases:
- Hand tracking in mobile applications
- Gesture recognition in edge devices
Deployment Options:
- TensorFlow Lite (.tflite export)
- QNN (.so export)
Performance:
- Estimated Inference Time:
- MediaPipeHandDetector:
0.93 ms - MediaPipeHandLandmarkDetector:
1.34 ms
- MediaPipeHandDetector:
- Estimated Peak Memory Range:
- MediaPipeHandDetector:
0.75-0.75 MB - MediaPipeHandLandmarkDetector:
0.75-0.75 MB
- MediaPipeHandDetector:
Capabilities
The MediaPipe-Hand-Detection model is designed for real-time hand detection on mobile and edge devices. It’s a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.
Primary Tasks
- Hand detection: The model can detect hands in an image and predict bounding boxes around them.
- Hand landmark detection: The model can also detect specific landmarks on the hand, such as the wrist, fingers, and palm.
Strengths
- Real-time performance: The model is optimized for mobile and edge devices, making it suitable for real-time applications.
- Low latency: The model has a low inference time, making it ideal for applications that require fast processing.
- Small model size: The model is relatively small in size, making it easy to deploy on devices with limited storage.
Unique Features
- Optimized for mobile: The model is specifically designed for mobile devices, making it a great choice for applications that require hand detection on-the-go.
- Support for multiple runtimes: The model can be deployed using multiple runtimes, including TensorFlow Lite and QNN.
Comparison to Other Models
| Model | Inference Time (ms) | Model Size (MB) |
|---|---|---|
| MediaPipe-Hand-Detection | 0.714 | 6.76 |
| ==Other Hand Detection Models== | 1.5-3.0 | 10-50 |
Note: The comparison table is just an example and may not reflect the actual performance of other models.
Example Use Cases
- Hand tracking in virtual reality applications
- Gesture recognition in gaming applications
- Hand detection in security cameras
Performance
MediaPipe Hand Detection is a high-performance model that excels in real-time hand detection tasks. Let’s dive into its impressive performance metrics.
Speed
| Device | Inference Time (ms) |
|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | 0.714 ms |
| Samsung Galaxy S23 Ultra (Android 13) | 1.048 ms |
| Snapdragon X Elite CRD (11) | 0.93 ms |
| Snapdragon X Elite CRD (11) | 1.34 ms |
As you can see, MediaPipe Hand Detection achieves incredibly fast inference times, making it suitable for real-time applications.
Accuracy
The model’s accuracy is also noteworthy. With a precision of FP16, it provides high-quality results in hand detection tasks.
Efficiency
| Device | Peak Memory Range (MB) |
|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | 0 - 5 MB |
| Samsung Galaxy S23 Ultra (Android 13) | 0 - 55 MB |
| Snapdragon X Elite CRD (11) | 0.75-0.75 MB |
| Snapdragon X Elite CRD (11) | 0.75-0.75 MB |
The model’s efficient memory usage makes it an excellent choice for deployment on edge devices.
Comparison to Other Models
While MediaPipe Hand Detection is an exceptional model, let’s compare its performance to other models in the same category. ==Other Hand Detection Models== may have higher accuracy or faster inference times, but they often come with larger model sizes and higher computational requirements.
In contrast, MediaPipe Hand Detection strikes a perfect balance between performance, efficiency, and model size, making it an ideal choice for a wide range of applications.
Limitations
MediaPipe Hand Detection is a powerful tool for real-time hand detection, but it has some limitations.
Limited Input Resolution
The model is optimized for an input resolution of 256x256 pixels. This means that it may not perform well on images with higher or lower resolutions.
Limited Number of Parameters
The MediaPipe Hand Detector has only 1.76M parameters, which is relatively small compared to other models. This may limit its ability to learn complex patterns and relationships in the data.
Limited Device Support
The model is currently optimized for Qualcomm devices, such as the Samsung Galaxy S23 Ultra. It may not perform well on other devices or platforms.
Limited Runtime Options
The model can only be run on TensorFlow Lite (TFLite) or QNN Model Library. This may limit its deployment options.
Limited Accuracy
The model’s accuracy may vary depending on the device, runtime, and input data. For example, the MediaPipe Hand Detector has an estimated inference time of 0.714 ms on the Samsung Galaxy S23 Ultra, but its accuracy may be lower on other devices.
Limited Support for Complex Scenarios
The model is designed for real-time hand detection, but it may not perform well in complex scenarios, such as:
- Multiple hands in the image
- Hands with complex gestures or poses
- Images with low lighting or noise
Limited Customization Options
The model is pre-trained and may not be easily customizable for specific use cases or applications.
These limitations highlight the need for further research and development to improve the MediaPipe Hand Detection model and make it more versatile and accurate.
Format
MediaPipe-Hand-Detection is an object detection model that predicts bounding boxes and pose skeletons of hands in an image. It’s optimized for mobile and edge devices.
Architecture
The model uses a machine learning pipeline to detect hands in images. It’s composed of two main components:
- MediaPipeHandDetector: This component detects the bounding box of the hand in the image.
- MediaPipeHandLandmarkDetector: This component predicts the pose skeleton of the hand within the detected bounding box.
Data Formats
The model accepts input images in the following formats:
| Format | Description |
|---|---|
RGB | 3-channel RGB image |
256x256 | Input resolution |
Input Requirements
To use this model, you’ll need to:
- Pre-process your input images to the required resolution (
256x256) - Convert your images to the
RGBformat
Output
The model outputs the detected bounding box and pose skeleton of the hand in the image.
Code Example
Here’s an example of how to use the model in Python:
import torch
from qai_hub_models.models.mediapipe_hand import MediaPipeHandDetector, MediaPipeHandLandmarkDetector
# Load the model
hand_detector_model = MediaPipeHandDetector.from_pretrained()
hand_landmark_detector_model = MediaPipeHandLandmarkDetector.from_pretrained()
# Pre-process your input image
input_image =... # Load your input image
input_image = torch.tensor(input_image) # Convert to tensor
input_image = input_image.resize((256, 256)) # Resize to required resolution
# Run the model
hand_detector_output = hand_detector_model(input_image)
hand_landmark_detector_output = hand_landmark_detector_model(hand_detector_output)
# Get the detected bounding box and pose skeleton
bounding_box = hand_detector_output['bounding_box']
pose_skeleton = hand_landmark_detector_output['pose_skeleton']
