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Facial-Landmark-Detection

Keypoint Detection
PyTorch
LiteRT
backbone
android
Model card Files Community

Facial-Landmark-Detection: Optimized for Mobile Deployment

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

Detects facial landmarks (eg, nose, mouth, etc.). This model's architecture was developed by Qualcomm. The model was trained by Qualcomm on a proprietary dataset of faces, but can be used on any image.

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: 5.42M
    • Model size (float): 20.7 MB
    • Model size (w8a8): 5.27 MB
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 1.143 ms 0 - 13 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 1.138 ms 0 - 12 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.384 ms 0 - 37 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.554 ms 0 - 22 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.279 ms 0 - 100 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.291 ms 0 - 41 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 0.492 ms 0 - 15 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 0.497 ms 0 - 13 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float SA7255P ADP Qualcomm® SA7255P TFLITE 1.143 ms 0 - 13 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA7255P ADP Qualcomm® SA7255P QNN_DLC 1.138 ms 0 - 12 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.282 ms 0 - 100 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.288 ms 0 - 52 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float SA8295P ADP Qualcomm® SA8295P TFLITE 0.654 ms 0 - 17 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8295P ADP Qualcomm® SA8295P QNN_DLC 0.612 ms 0 - 15 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.283 ms 0 - 98 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.292 ms 0 - 48 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float SA8775P ADP Qualcomm® SA8775P TFLITE 0.492 ms 0 - 15 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float SA8775P ADP Qualcomm® SA8775P QNN_DLC 0.497 ms 0 - 13 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.283 ms 0 - 99 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.284 ms 0 - 49 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.403 ms 0 - 47 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.22 ms 0 - 31 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.216 ms 0 - 20 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.314 ms 0 - 22 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.207 ms 0 - 19 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.219 ms 0 - 15 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.318 ms 0 - 12 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.367 ms 38 - 38 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.387 ms 10 - 10 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 0.455 ms 0 - 12 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 0.414 ms 0 - 10 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.217 ms 0 - 27 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.235 ms 0 - 32 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.173 ms 0 - 42 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.165 ms 0 - 32 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 0.329 ms 0 - 11 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 0.307 ms 0 - 14 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 0.539 ms 0 - 22 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 0.6 ms 0 - 22 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 1.647 ms 0 - 2 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 0.455 ms 0 - 12 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 0.414 ms 0 - 10 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.17 ms 0 - 43 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.159 ms 0 - 42 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 0.425 ms 0 - 21 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 0.423 ms 0 - 15 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.166 ms 0 - 42 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.165 ms 0 - 42 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 0.329 ms 0 - 11 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 0.307 ms 0 - 14 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.178 ms 0 - 43 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.167 ms 0 - 43 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.292 ms 0 - 15 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.134 ms 0 - 30 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.135 ms 0 - 32 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.212 ms 0 - 33 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.137 ms 0 - 18 MB NPU Facial-Landmark-Detection.tflite
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.121 ms 0 - 17 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.236 ms 0 - 19 MB NPU Facial-Landmark-Detection.onnx
Facial-Landmark-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.244 ms 32 - 32 MB NPU Facial-Landmark-Detection.dlc
Facial-Landmark-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.281 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)   : 1.1                                 
Estimated peak memory usage (MB): [0, 13]                             
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 --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.facemap_3dmm.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 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

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