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Sponsored by
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qualcomm / Facial-Landmark-Detection-Quantized
Model's Last Updated: April 10 2025
keypoint-detection
Introduction of Facial-Landmark-Detection-Quantized
Model Details of Facial-Landmark-Detection-Quantized
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 (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application. -
QNN (
.soexport ): This sample app provides instructions on how to use the.soshared 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 .
Runs of qualcomm Facial-Landmark-Detection-Quantized on huggingface.co
396
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More Information About Facial-Landmark-Detection-Quantized huggingface.co Model
Url of Facial-Landmark-Detection-Quantized
Facial-Landmark-Detection-Quantized huggingface.co Url
Provider of Facial-Landmark-Detection-Quantized huggingface.co
Other API from qualcomm
Updated:May 16 2025
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