Facial-Landmark-Detection-Quantized: Optimized for Mobile Deployment

Facial landmark predictor with 3DMM

Real-time 3D facial landmark detection optimized for mobile and edge.

This model is an implementation of Facial-Landmark-Detection-Quantized found here.

This repository provides scripts to run Facial-Landmark-Detection-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

Model Type: Pose estimation
Model Stats:
- Input resolution: 128x128
- Number of parameters: 5.424M
- Model size: 5.314MB

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Facial-Landmark-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	0.237 ms	0 - 5 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.so
Facial-Landmark-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.191 ms	0 - 9 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.so
Facial-Landmark-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.182 ms	0 - 9 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	QNN	0.81 ms	0 - 7 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	QNN	0.228 ms	0 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA7255P ADP	SA7255P	QNN	1.727 ms	0 - 5 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8255 (Proxy)	SA8255P Proxy	QNN	0.23 ms	0 - 2 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8295P ADP	SA8295P	QNN	0.661 ms	0 - 6 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8650 (Proxy)	SA8650P Proxy	QNN	0.228 ms	0 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8775P ADP	SA8775P	QNN	0.677 ms	0 - 6 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	QNN	0.283 ms	0 - 12 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	0.335 ms	1 - 1 MB	INT8	NPU	Use Export Script

Installation

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

pip install "qai-hub-models[facemap_3dmm_quantized]"

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.facemap_3dmm_quantized.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.facemap_3dmm_quantized.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.facemap_3dmm_quantized.export

Profiling Results
------------------------------------------------------------
Facial-Landmark-Detection-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 0.2                    
Estimated peak memory usage (MB): [0, 5]                 
Total # Ops                     : 55                     
Compute Unit(s)                 : NPU (55 ops)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.facemap_3dmm_quantized.demo --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.facemap_3dmm_quantized.demo -- --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 Facial-Landmark-Detection-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Facial-Landmark-Detection-Quantized can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

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For questions or feedback please reach out to us.