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	TFLITE	0.169 ms	0 - 94 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	0.232 ms	0 - 6 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.so
Facial-Landmark-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	0.338 ms	0 - 108 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.onnx
Facial-Landmark-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	0.141 ms	0 - 10 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.185 ms	0 - 10 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.so
Facial-Landmark-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	0.271 ms	0 - 29 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.onnx
Facial-Landmark-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	0.126 ms	0 - 9 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.176 ms	0 - 10 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	0.274 ms	0 - 17 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.onnx
Facial-Landmark-Detection-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	TFLITE	0.519 ms	0 - 12 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	QNN	0.701 ms	0 - 7 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	RB5 (Proxy)	QCS8250 Proxy	TFLITE	1.87 ms	0 - 3 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	0.175 ms	0 - 6 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	QNN	0.218 ms	0 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA7255P ADP	SA7255P	TFLITE	1.191 ms	0 - 12 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	SA7255P ADP	SA7255P	QNN	1.415 ms	0 - 10 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8255 (Proxy)	SA8255P Proxy	TFLITE	0.173 ms	0 - 6 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	SA8255 (Proxy)	SA8255P Proxy	QNN	0.219 ms	0 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8295P ADP	SA8295P	TFLITE	0.452 ms	0 - 8 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	SA8295P ADP	SA8295P	QNN	0.78 ms	0 - 6 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8650 (Proxy)	SA8650P Proxy	TFLITE	0.169 ms	0 - 6 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	SA8650 (Proxy)	SA8650P Proxy	QNN	0.219 ms	0 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	SA8775P ADP	SA8775P	TFLITE	0.382 ms	0 - 12 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	SA8775P ADP	SA8775P	QNN	0.581 ms	0 - 6 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	0.217 ms	0 - 12 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.tflite
Facial-Landmark-Detection-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	QNN	0.28 ms	0 - 15 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	0.298 ms	1 - 1 MB	INT8	NPU	Use Export Script
Facial-Landmark-Detection-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	0.353 ms	7 - 7 MB	INT8	NPU	Facial-Landmark-Detection-Quantized.onnx

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                         : TFLITE                 
Estimated inference time (ms)   : 0.2                    
Estimated peak memory usage (MB): [0, 94]                
Total # Ops                     : 43                     
Compute Unit(s)                 : NPU (43 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

Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
For questions or feedback please reach out to us.