qualcomm / Facial-Attribute-Detection-Quantized

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

Comprehensive facial analysis by extracting face features

Facial feature extraction and additional attributes including liveness, eyeclose, mask and glasses detection for face recognition.

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

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

Model Details

• Model Type: Object detection
• Model Stats:
  • Model checkpoint: multitask_FR_state_dict.pt
  • Input resolution: 128x128
  • Input channel number: 1
  • Number of parameters: 11.6M
  • Model size: 47.6MB

Installation

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

pip install qai-hub-models

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.face_attrib_net_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.face_attrib_net_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.face_attrib_net_quantized.export

Profiling Results
------------------------------------------------------------
Facial-Attribute-Detection-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 0.5                    
Estimated peak memory usage (MB): [0, 9]                 
Total # Ops                     : 170                    
Compute Unit(s)                 : NPU (170 ops)          

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.face_attrib_net_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.face_attrib_net_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-Attribute-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-Attribute-Detection-Quantized can be found here .
• The license for the compiled assets for on-device deployment can be found here

References

• None
• Source Model Implementation

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 .