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Facial-Landmark-Detection

Keypoint Detection
PyTorch
LiteRT
ONNX
backbone
android
Model card Files Community

Facial-Landmark-Detection: 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 found here.

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

Model Details

  • Model Type: Model_use_case.pose_estimation
  • Model Stats:
    • Input resolution: 128x128
    • Number of parameters: 5.424M
    • Model size (float): 21.256MB
    • Model size (w8a8): 5.314MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
Facial-Landmark-Detection float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 1.157 ms 0 - 12 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 1.146 ms 0 - 9 MB NPU Use Export Script
Facial-Landmark-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.478 ms 0 - 32 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 0.543 ms 0 - 21 MB NPU Use Export Script
Facial-Landmark-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.287 ms 0 - 94 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 0.281 ms 0 - 2 MB NPU Use Export Script
Facial-Landmark-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 0.518 ms 0 - 16 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 0.497 ms 0 - 14 MB NPU Use Export Script
Facial-Landmark-Detection float SA7255P ADP Qualcomm® SA7255P TFLITE 1.157 ms 0 - 12 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA7255P ADP Qualcomm® SA7255P QNN 1.146 ms 0 - 9 MB NPU Use Export Script
Facial-Landmark-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.28 ms 0 - 96 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 0.282 ms 0 - 2 MB NPU Use Export Script
Facial-Landmark-Detection float SA8295P ADP Qualcomm® SA8295P TFLITE 0.66 ms 0 - 18 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8295P ADP Qualcomm® SA8295P QNN 0.624 ms 0 - 18 MB NPU Use Export Script
Facial-Landmark-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.287 ms 0 - 97 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 0.284 ms 0 - 2 MB NPU Use Export Script
Facial-Landmark-Detection float SA8775P ADP Qualcomm® SA8775P TFLITE 0.518 ms 0 - 16 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8775P ADP Qualcomm® SA8775P QNN 0.497 ms 0 - 14 MB NPU Use Export Script
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.279 ms 0 - 95 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 0.277 ms 0 - 27 MB NPU Use Export Script
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.391 ms 0 - 45 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.223 ms 0 - 34 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.217 ms 0 - 19 MB NPU Use Export Script
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.305 ms 0 - 21 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.21 ms 0 - 15 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.195 ms 0 - 16 MB NPU Use Export Script
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.286 ms 0 - 18 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.322 ms 0 - 0 MB NPU Use Export Script
Facial-Landmark-Detection float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.36 ms 12 - 12 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 0.463 ms 0 - 11 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 0.444 ms 0 - 10 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.278 ms 0 - 28 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 0.303 ms 0 - 27 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.179 ms 0 - 37 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 0.161 ms 0 - 3 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 0.345 ms 0 - 14 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 0.321 ms 0 - 15 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 0.528 ms 0 - 24 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 0.583 ms 0 - 14 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 1.605 ms 0 - 2 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 0.463 ms 0 - 11 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA7255P ADP Qualcomm® SA7255P QNN 0.444 ms 0 - 10 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.167 ms 0 - 36 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 0.17 ms 0 - 3 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 0.465 ms 0 - 16 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8295P ADP Qualcomm® SA8295P QNN 0.439 ms 0 - 18 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.175 ms 0 - 37 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 0.177 ms 0 - 2 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 0.345 ms 0 - 14 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8775P ADP Qualcomm® SA8775P QNN 0.321 ms 0 - 15 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.164 ms 0 - 37 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 0.17 ms 0 - 36 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.435 ms 0 - 16 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.142 ms 0 - 31 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.128 ms 0 - 30 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.349 ms 0 - 33 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.152 ms 0 - 21 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.134 ms 0 - 15 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.385 ms 0 - 21 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.215 ms 1 - 1 MB NPU Use Export Script
Facial-Landmark-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.458 ms 4 - 4 MB NPU Facial-Landmark-Detection.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[facemap-3dmm]"

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.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.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.export

Profiling Results
------------------------------------------------------------
Facial-Landmark-Detection
Device                          : cs_8275 (ANDROID 14)                
Runtime                         : TFLITE                              
Estimated inference time (ms)   : 1.2                                 
Estimated peak memory usage (MB): [0, 12]                             
Total # Ops                     : 37                                  
Compute Unit(s)                 : npu (37 ops) gpu (0 ops) cpu (0 ops)

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.facemap_3dmm import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# 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. 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.facemap_3dmm.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.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'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 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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