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MediaPipe-Hand-Detection

Object Detection
PyTorch
TF Lite
ONNX
real_time
android
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metadata
library_name: pytorch
license: apache-2.0
pipeline_tag: object-detection
tags:
  - real_time
  - android

MediaPipe-Hand-Detection: Optimized for Mobile Deployment

Real-time hand detection optimized for mobile and edge

The MediaPipe Hand Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.

This model is an implementation of MediaPipe-Hand-Detection found here. This repository provides scripts to run MediaPipe-Hand-Detection on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Object detection
  • Model Stats:
    • Input resolution: 256x256
    • Number of parameters (MediaPipeHandDetector): 1.76M
    • Model size (MediaPipeHandDetector): 6.76 MB
    • Number of parameters (MediaPipeHandLandmarkDetector): 2.01M
    • Model size (MediaPipeHandLandmarkDetector): 7.71 MB
Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 TFLite 0.714 ms 0 - 5 MB FP16 NPU MediaPipeHandDetector.tflite
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 TFLite 1.048 ms 0 - 55 MB FP16 NPU MediaPipeHandLandmarkDetector.tflite
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 QNN Model Library 0.791 ms 1 - 20 MB FP16 NPU MediaPipeHandDetector.so
Samsung Galaxy S23 Ultra (Android 13) Snapdragon® 8 Gen 2 QNN Model Library 1.109 ms 2 - 39 MB FP16 NPU MediaPipeHandLandmarkDetector.so

Installation

This model can be installed as a Python package via pip.

pip install qai-hub-models

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.mediapipe_hand.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.mediapipe_hand.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.mediapipe_hand.export

Profile Job summary of MediaPipeHandDetector
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 0.93 ms
Estimated Peak Memory Range: 0.75-0.75 MB
Compute Units: NPU (195) | Total (195)

Profile Job summary of MediaPipeHandLandmarkDetector
--------------------------------------------------
Device: Snapdragon X Elite CRD (11)
Estimated Inference Time: 1.34 ms
Estimated Peak Memory Range: 0.75-0.75 MB
Compute Units: NPU (208) | Total (208)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
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()

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
hand_detector_input_shape = hand_detector_model.get_input_spec()
hand_detector_sample_inputs = hand_detector_model.sample_inputs()

traced_hand_detector_model = torch.jit.trace(hand_detector_model, [torch.tensor(data[0]) for _, data in hand_detector_sample_inputs.items()])

# Compile model on a specific device
hand_detector_compile_job = hub.submit_compile_job(
    model=traced_hand_detector_model ,
    device=device,
    input_specs=hand_detector_model.get_input_spec(),
)

# Get target model to run on-device
hand_detector_target_model = hand_detector_compile_job.get_target_model()
# Trace model
hand_landmark_detector_input_shape = hand_landmark_detector_model.get_input_spec()
hand_landmark_detector_sample_inputs = hand_landmark_detector_model.sample_inputs()

traced_hand_landmark_detector_model = torch.jit.trace(hand_landmark_detector_model, [torch.tensor(data[0]) for _, data in hand_landmark_detector_sample_inputs.items()])

# Compile model on a specific device
hand_landmark_detector_compile_job = hub.submit_compile_job(
    model=traced_hand_landmark_detector_model ,
    device=device,
    input_specs=hand_landmark_detector_model.get_input_spec(),
)

# Get target model to run on-device
hand_landmark_detector_target_model = hand_landmark_detector_compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

hand_detector_profile_job = hub.submit_profile_job(
    model=hand_detector_target_model,
    device=device,
)
hand_landmark_detector_profile_job = hub.submit_profile_job(
    model=hand_landmark_detector_target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

hand_detector_input_data = hand_detector_model.sample_inputs()
hand_detector_inference_job = hub.submit_inference_job(
    model=hand_detector_target_model,
    device=device,
    inputs=hand_detector_input_data,
)
hand_detector_inference_job.download_output_data()
hand_landmark_detector_input_data = hand_landmark_detector_model.sample_inputs()
hand_landmark_detector_inference_job = hub.submit_inference_job(
    model=hand_landmark_detector_target_model,
    device=device,
    inputs=hand_landmark_detector_input_data,
)
hand_landmark_detector_inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on MediaPipe-Hand-Detection's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of MediaPipe-Hand-Detection can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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