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HRNetFace

Object Detection
PyTorch
LiteRT
real_time
android
Model card Files
xet
Community

HRNetFace: Optimized for Mobile Deployment

Comprehensive facial analysis by extracting face features

Detects attributes (liveness, eye closeness, mask presence, glasses presence, sunglasses presence) that apply to a given face.

This model is an implementation of HRNetFace found here.

This repository provides scripts to run HRNetFace on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.object_detection
  • Model Stats:
    • Model checkpoint: HR18-COFW.pth
    • Input resolution: 256x256
    • Number of parameters: 9.68M
    • Model size (float): 36.87MB
    • Model size (w8a8): 17.7 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
HRNetFace float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 15.597 ms 0 - 60 MB NPU HRNetFace.tflite
HRNetFace float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 15.505 ms 1 - 46 MB NPU HRNetFace.dlc
HRNetFace float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 4.483 ms 0 - 63 MB NPU HRNetFace.tflite
HRNetFace float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 4.846 ms 1 - 48 MB NPU HRNetFace.dlc
HRNetFace float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 3.141 ms 0 - 132 MB NPU HRNetFace.tflite
HRNetFace float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 3.233 ms 0 - 13 MB NPU HRNetFace.dlc
HRNetFace float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 3.322 ms 0 - 45 MB NPU HRNetFace.onnx.zip
HRNetFace float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 21.582 ms 0 - 60 MB NPU HRNetFace.tflite
HRNetFace float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 5.119 ms 1 - 48 MB NPU HRNetFace.dlc
HRNetFace float SA7255P ADP Qualcomm® SA7255P TFLITE 15.597 ms 0 - 60 MB NPU HRNetFace.tflite
HRNetFace float SA7255P ADP Qualcomm® SA7255P QNN_DLC 15.505 ms 1 - 46 MB NPU HRNetFace.dlc
HRNetFace float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 3.151 ms 0 - 131 MB NPU HRNetFace.tflite
HRNetFace float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 3.241 ms 1 - 14 MB NPU HRNetFace.dlc
HRNetFace float SA8295P ADP Qualcomm® SA8295P TFLITE 5.53 ms 0 - 54 MB NPU HRNetFace.tflite
HRNetFace float SA8295P ADP Qualcomm® SA8295P QNN_DLC 5.587 ms 1 - 45 MB NPU HRNetFace.dlc
HRNetFace float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 3.146 ms 0 - 129 MB NPU HRNetFace.tflite
HRNetFace float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 3.247 ms 1 - 14 MB NPU HRNetFace.dlc
HRNetFace float SA8775P ADP Qualcomm® SA8775P TFLITE 21.582 ms 0 - 60 MB NPU HRNetFace.tflite
HRNetFace float SA8775P ADP Qualcomm® SA8775P QNN_DLC 5.119 ms 1 - 48 MB NPU HRNetFace.dlc
HRNetFace float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 2.219 ms 0 - 66 MB NPU HRNetFace.tflite
HRNetFace float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 2.336 ms 0 - 51 MB NPU HRNetFace.dlc
HRNetFace float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 2.324 ms 0 - 68 MB NPU HRNetFace.onnx.zip
HRNetFace float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 1.748 ms 0 - 63 MB NPU HRNetFace.tflite
HRNetFace float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 1.776 ms 1 - 52 MB NPU HRNetFace.dlc
HRNetFace float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 1.85 ms 0 - 56 MB NPU HRNetFace.onnx.zip
HRNetFace float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 1.417 ms 0 - 62 MB NPU HRNetFace.tflite
HRNetFace float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 1.408 ms 0 - 52 MB NPU HRNetFace.dlc
HRNetFace float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 1.558 ms 1 - 58 MB NPU HRNetFace.onnx.zip
HRNetFace float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 3.628 ms 27 - 27 MB NPU HRNetFace.dlc
HRNetFace float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 3.257 ms 30 - 30 MB NPU HRNetFace.onnx.zip
HRNetFace w8a8 Dragonwing RB3 Gen 2 Vision Kit Qualcomm® QCS6490 TFLITE 3.535 ms 0 - 18 MB NPU HRNetFace.tflite
HRNetFace w8a8 Dragonwing RB3 Gen 2 Vision Kit Qualcomm® QCS6490 QNN_DLC 3.72 ms 0 - 108 MB NPU HRNetFace.dlc
HRNetFace w8a8 Dragonwing RB3 Gen 2 Vision Kit Qualcomm® QCS6490 ONNX 91.59 ms 18 - 35 MB CPU HRNetFace.onnx.zip
HRNetFace w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 3.265 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 3.38 ms 0 - 46 MB NPU HRNetFace.dlc
HRNetFace w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 1.456 ms 0 - 60 MB NPU HRNetFace.tflite
HRNetFace w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 1.907 ms 0 - 56 MB NPU HRNetFace.dlc
HRNetFace w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 1.295 ms 0 - 47 MB NPU HRNetFace.tflite
HRNetFace w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 1.38 ms 0 - 17 MB NPU HRNetFace.dlc
HRNetFace w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 1.647 ms 0 - 21 MB NPU HRNetFace.onnx.zip
HRNetFace w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.721 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 6.892 ms 0 - 45 MB NPU HRNetFace.dlc
HRNetFace w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 19.484 ms 0 - 3 MB NPU HRNetFace.tflite
HRNetFace w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) ONNX 46.443 ms 16 - 31 MB CPU HRNetFace.onnx.zip
HRNetFace w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 3.265 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 3.38 ms 0 - 46 MB NPU HRNetFace.dlc
HRNetFace w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 1.298 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 1.369 ms 0 - 18 MB NPU HRNetFace.dlc
HRNetFace w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 2.139 ms 0 - 53 MB NPU HRNetFace.tflite
HRNetFace w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 2.272 ms 0 - 53 MB NPU HRNetFace.dlc
HRNetFace w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 1.301 ms 0 - 45 MB NPU HRNetFace.tflite
HRNetFace w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 1.376 ms 0 - 18 MB NPU HRNetFace.dlc
HRNetFace w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 1.721 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 6.892 ms 0 - 45 MB NPU HRNetFace.dlc
HRNetFace w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.884 ms 0 - 65 MB NPU HRNetFace.tflite
HRNetFace w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.952 ms 0 - 59 MB NPU HRNetFace.dlc
HRNetFace w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.071 ms 0 - 74 MB NPU HRNetFace.onnx.zip
HRNetFace w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 0.715 ms 0 - 46 MB NPU HRNetFace.tflite
HRNetFace w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 0.67 ms 0 - 52 MB NPU HRNetFace.dlc
HRNetFace w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 0.886 ms 0 - 58 MB NPU HRNetFace.onnx.zip
HRNetFace w8a8 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile TFLITE 1.533 ms 0 - 55 MB NPU HRNetFace.tflite
HRNetFace w8a8 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile QNN_DLC 1.66 ms 0 - 57 MB NPU HRNetFace.dlc
HRNetFace w8a8 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile ONNX 45.713 ms 20 - 45 MB CPU HRNetFace.onnx.zip
HRNetFace w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 0.605 ms 0 - 53 MB NPU HRNetFace.tflite
HRNetFace w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 0.55 ms 0 - 52 MB NPU HRNetFace.dlc
HRNetFace w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 0.792 ms 0 - 60 MB NPU HRNetFace.onnx.zip
HRNetFace w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 1.615 ms 36 - 36 MB NPU HRNetFace.dlc
HRNetFace w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.545 ms 15 - 15 MB NPU HRNetFace.onnx.zip

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[hrnet-face]"

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

Sign-in to Qualcomm® AI Hub Workbench 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.hrnet_face.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.hrnet_face.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.hrnet_face.export

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.hrnet_face import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = 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.

profile_job = hub.submit_profile_job(
    model=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.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = 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 Workbench. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.hrnet_face.demo --eval-mode on-device

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.hrnet_face.demo -- --eval-mode on-device

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 HRNetFace's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

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