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Model Details
Model Description
The MediaPipe Palm detection model is a single-shot detector optimized for mobile realtime applications. The model detects palms instead of entire hands since estimating bounding boxes of rigid objects like palms and fists is significantly simpler than detecting hands with articulated fingers.
Developed by: Google
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
For training the palm detector, in-the-wild dataset was used, which is sufficient for localizing hands and offers the highest variety in appearance.
In-the-wild dataset contains 6K images of a large variety, e.g. geographical diversity, various lighting conditions, and hand appearance. The limitation of this dataset is that it doesn’t contain complex articulation of hands.
Testing Details
Metrics
Evaluation results are taken from .
Metric
Value
Average Precision
95.7%
Technical Specifications
Input/Output Details
Input:
Name: input_1
Info: NCHW BGR un-normalized image
Output:
Name: multiple (see NN archive)
Info: Hand bounding boxes and classification scores
Model Architecture
BlazePalm single-shot detector and encoder-decoder feature extractor similar to FPN.
Please consult for more information on model architecture.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 version of the model was quantized using a custom dataset.
This was created by taking 30 images from the web where the hands of the people are present.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
MPPalmDetectionParser that outputs message (bounding boxes of detected hands with confidence scores).
Get parsed output(s):
while pipeline.isRuning():
parser_output: ImgDetectionsExtended = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
