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Facial-Attribute-Detection-Quantized

Object Detection
PyTorch
TF Lite
ONNX
real_time
quantized
android
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metadata
library_name: pytorch
license: bsd-3-clause
pipeline_tag: object-detection
tags:
  - real_time
  - quantized
  - android

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
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
Facial-Attribute-Detection-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 0.498 ms 0 - 9 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 0.515 ms 0 - 19 MB INT8 NPU Facial-Attribute-Detection-Quantized.so
Facial-Attribute-Detection-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 0.912 ms 0 - 16 MB INT8 NPU Facial-Attribute-Detection-Quantized.onnx
Facial-Attribute-Detection-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 0.376 ms 0 - 29 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 0.38 ms 0 - 26 MB INT8 NPU Facial-Attribute-Detection-Quantized.so
Facial-Attribute-Detection-Quantized Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 0.68 ms 0 - 102 MB INT8 NPU Facial-Attribute-Detection-Quantized.onnx
Facial-Attribute-Detection-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 0.38 ms 0 - 19 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 0.402 ms 0 - 20 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 0.659 ms 0 - 52 MB INT8 NPU Facial-Attribute-Detection-Quantized.onnx
Facial-Attribute-Detection-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy TFLITE 2.656 ms 0 - 32 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy QNN 1.775 ms 0 - 12 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized RB5 (Proxy) QCS8250 Proxy TFLITE 54.45 ms 2 - 5 MB FP32 CPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized QCS8550 (Proxy) QCS8550 Proxy TFLITE 0.504 ms 0 - 9 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized QCS8550 (Proxy) QCS8550 Proxy QNN 0.48 ms 0 - 1 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized SA7255P ADP SA7255P TFLITE 5.066 ms 0 - 21 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized SA7255P ADP SA7255P QNN 5.24 ms 0 - 10 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized SA8255 (Proxy) SA8255P Proxy TFLITE 0.502 ms 0 - 9 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized SA8255 (Proxy) SA8255P Proxy QNN 0.488 ms 0 - 2 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized SA8295P ADP SA8295P TFLITE 1.022 ms 0 - 19 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized SA8295P ADP SA8295P QNN 1.07 ms 0 - 6 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized SA8650 (Proxy) SA8650P Proxy TFLITE 0.504 ms 0 - 10 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized SA8650 (Proxy) SA8650P Proxy QNN 0.484 ms 0 - 1 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized SA8775P ADP SA8775P TFLITE 0.919 ms 0 - 23 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized SA8775P ADP SA8775P QNN 1.021 ms 0 - 6 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized QCS8450 (Proxy) QCS8450 Proxy TFLITE 0.641 ms 0 - 32 MB INT8 NPU Facial-Attribute-Detection-Quantized.tflite
Facial-Attribute-Detection-Quantized QCS8450 (Proxy) QCS8450 Proxy QNN 0.637 ms 0 - 26 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.628 ms 1 - 1 MB INT8 NPU Use Export Script
Facial-Attribute-Detection-Quantized Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.026 ms 15 - 15 MB INT8 NPU Facial-Attribute-Detection-Quantized.onnx

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

Community