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Posenet-Mobilenet-Quantized

Keypoint Detection
PyTorch
TF Lite
quantized
android
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metadata
library_name: pytorch
license: apache-2.0
pipeline_tag: keypoint-detection
tags:
  - quantized
  - android

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 0.559 ms 0 - 2 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 0.633 ms 0 - 5 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 0.442 ms 0 - 20 MB INT8 NPU Posenet-Mobilenet-Quantized.so
Posenet-Mobilenet-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 0.347 ms 0 - 27 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 0.392 ms 0 - 17 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy TFLITE 2.191 ms 0 - 27 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized RB3 Gen 2 (Proxy) QCS6490 Proxy QNN 2.87 ms 0 - 8 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized RB5 (Proxy) QCS8250 Proxy TFLITE 13.626 ms 0 - 8 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized QCS8550 (Proxy) QCS8550 Proxy TFLITE 0.557 ms 0 - 1 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized QCS8550 (Proxy) QCS8550 Proxy QNN 0.556 ms 0 - 2 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized SA8255 (Proxy) SA8255P Proxy TFLITE 0.557 ms 0 - 1 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized SA8255 (Proxy) SA8255P Proxy QNN 0.563 ms 0 - 2 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized SA8775 (Proxy) SA8775P Proxy TFLITE 0.556 ms 0 - 96 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized SA8775 (Proxy) SA8775P Proxy QNN 0.555 ms 0 - 2 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized SA8650 (Proxy) SA8650P Proxy TFLITE 0.56 ms 0 - 1 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized SA8650 (Proxy) SA8650P Proxy QNN 0.554 ms 0 - 2 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized SA8295P ADP SA8295P TFLITE 1.212 ms 0 - 25 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized SA8295P ADP SA8295P QNN 1.275 ms 0 - 6 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized QCS8450 (Proxy) QCS8450 Proxy TFLITE 0.722 ms 0 - 49 MB INT8 NPU Posenet-Mobilenet-Quantized.tflite
Posenet-Mobilenet-Quantized QCS8450 (Proxy) QCS8450 Proxy QNN 0.804 ms 0 - 19 MB INT8 NPU Use Export Script
Posenet-Mobilenet-Quantized Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.714 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, 2]                 
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