Posenet-Mobilenet-Quantized: Optimized for Mobile Deployment

Quantized human pose estimator

Posenet performs pose estimation on human images.

This model is an implementation of Posenet-Mobilenet-Quantized found here.

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

Model Details

Model Type: Pose estimation
Model Stats:
- Model checkpoint: mobilenet_v1_101
- Input resolution: 513x257
- Number of parameters: 3.31M
- Model size: 3.47 MB

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Posenet-Mobilenet-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	0.557 ms	0 - 104 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	0.64 ms	0 - 8 MB	INT8	NPU	Posenet-Mobilenet-Quantized.so
Posenet-Mobilenet-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	0.393 ms	0 - 49 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.441 ms	0 - 18 MB	INT8	NPU	Posenet-Mobilenet-Quantized.so
Posenet-Mobilenet-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	0.338 ms	0 - 27 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.458 ms	0 - 17 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	TFLITE	2.178 ms	0 - 27 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	QNN	3.09 ms	0 - 8 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	RB5 (Proxy)	QCS8250 Proxy	TFLITE	13.019 ms	0 - 11 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	0.554 ms	0 - 1 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	QNN	0.554 ms	0 - 2 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	SA8255 (Proxy)	SA8255P Proxy	TFLITE	0.562 ms	0 - 1 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	SA8255 (Proxy)	SA8255P Proxy	QNN	0.561 ms	0 - 2 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	SA8775 (Proxy)	SA8775P Proxy	TFLITE	0.561 ms	0 - 106 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	SA8775 (Proxy)	SA8775P Proxy	QNN	0.557 ms	0 - 2 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	SA8650 (Proxy)	SA8650P Proxy	TFLITE	0.557 ms	0 - 1 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	SA8650 (Proxy)	SA8650P Proxy	QNN	0.57 ms	0 - 1 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	SA8295P ADP	SA8295P	TFLITE	1.226 ms	0 - 24 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	SA8295P ADP	SA8295P	QNN	1.281 ms	0 - 6 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	0.719 ms	0 - 49 MB	INT8	NPU	Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	QNN	0.781 ms	0 - 21 MB	INT8	NPU	Use Export Script
Posenet-Mobilenet-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	0.692 ms	0 - 0 MB	INT8	NPU	Use Export Script

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.posenet_mobilenet_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.posenet_mobilenet_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.posenet_mobilenet_quantized.export

Profiling Results
------------------------------------------------------------
Posenet-Mobilenet-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 0.6                    
Estimated peak memory usage (MB): [0, 104]               
Total # Ops                     : 48                     
Compute Unit(s)                 : NPU (48 ops)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.posenet_mobilenet_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.posenet_mobilenet_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 Posenet-Mobilenet-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Posenet-Mobilenet-Quantized can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

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For questions or feedback please reach out to us.