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 | 0.439 ms | 0 - 31 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 0.51 ms | 0 - 31 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.so |
| Facial-Attribute-Detection-Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 0.939 ms | 0 - 40 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.onnx |
| Facial-Attribute-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 0.329 ms | 0 - 35 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 0.381 ms | 0 - 25 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.so |
| Facial-Attribute-Detection-Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 0.684 ms | 0 - 40 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.onnx |
| Facial-Attribute-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 0.281 ms | 0 - 28 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 0.327 ms | 0 - 28 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 0.68 ms | 0 - 35 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.onnx |
| Facial-Attribute-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | TFLITE | 1.419 ms | 0 - 26 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | RB3 Gen 2 (Proxy) | QCS6490 Proxy | QNN | 1.749 ms | 0 - 11 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | RB5 (Proxy) | QCS8250 Proxy | TFLITE | 79.556 ms | 2 - 5 MB | FP32 | CPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 0.447 ms | 0 - 23 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 0.485 ms | 0 - 2 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | SA7255P ADP | SA7255P | TFLITE | 4.849 ms | 0 - 21 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | SA7255P ADP | SA7255P | QNN | 5.044 ms | 0 - 10 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 0.446 ms | 0 - 31 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | SA8255 (Proxy) | SA8255P Proxy | QNN | 0.483 ms | 0 - 3 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | SA8295P ADP | SA8295P | TFLITE | 0.939 ms | 0 - 27 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | SA8295P ADP | SA8295P | QNN | 1.229 ms | 0 - 14 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 0.45 ms | 0 - 30 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | SA8650 (Proxy) | SA8650P Proxy | QNN | 0.487 ms | 0 - 3 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | SA8775P ADP | SA8775P | TFLITE | 0.821 ms | 0 - 20 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | SA8775P ADP | SA8775P | QNN | 1.045 ms | 0 - 10 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 0.569 ms | 0 - 27 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.tflite |
| Facial-Attribute-Detection-Quantized | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 0.636 ms | 0 - 28 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 0.615 ms | 1 - 1 MB | INT8 | NPU | Use Export Script |
| Facial-Attribute-Detection-Quantized | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.022 ms | 15 - 15 MB | INT8 | NPU | Facial-Attribute-Detection-Quantized.onnx |
Installation
Install the 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) : 0.4
Estimated peak memory usage (MB): [0, 31]
Total # Ops : 168
Compute Unit(s) : NPU (168 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 (
.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-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
- 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.