Person-Foot-Detection-Quantized: Optimized for Mobile Deployment

Multi-task Human detector

Real-time multiple person detection with accurate feet localization optimized for mobile and edge.

This model is an implementation of Person-Foot-Detection-Quantized found here.

This repository provides scripts to run Person-Foot-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: SA-e30_finetune50.pth
- Inference latency: RealTime
- Input resolution: 640x480
- Number of output classes: 2
- Number of parameters: 2.53M
- Model size: 9.69 MB

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Person-Foot-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	1.15 ms	0 - 1 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	1.287 ms	1 - 8 MB	INT8	NPU	Person-Foot-Detection-Quantized.so
Person-Foot-Detection-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	1.637 ms	0 - 4 MB	INT8	NPU	Person-Foot-Detection-Quantized.onnx
Person-Foot-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	0.798 ms	0 - 49 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.882 ms	1 - 25 MB	INT8	NPU	Person-Foot-Detection-Quantized.so
Person-Foot-Detection-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	1.133 ms	0 - 57 MB	INT8	NPU	Person-Foot-Detection-Quantized.onnx
Person-Foot-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	0.684 ms	0 - 33 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.831 ms	0 - 21 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	1.121 ms	0 - 40 MB	INT8	NPU	Person-Foot-Detection-Quantized.onnx
Person-Foot-Detection-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	TFLITE	5.372 ms	0 - 36 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	QNN	6.758 ms	1 - 9 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	RB5 (Proxy)	QCS8250 Proxy	TFLITE	26.589 ms	1 - 8 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	1.137 ms	0 - 1 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	QNN	1.226 ms	1 - 2 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	SA8255 (Proxy)	SA8255P Proxy	TFLITE	1.152 ms	0 - 5 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	SA8255 (Proxy)	SA8255P Proxy	QNN	1.239 ms	1 - 4 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	SA8775 (Proxy)	SA8775P Proxy	TFLITE	1.15 ms	0 - 1 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	SA8775 (Proxy)	SA8775P Proxy	QNN	1.24 ms	1 - 2 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	SA8650 (Proxy)	SA8650P Proxy	TFLITE	1.189 ms	0 - 1 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	SA8650 (Proxy)	SA8650P Proxy	QNN	1.268 ms	1 - 2 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	SA8295P ADP	SA8295P	TFLITE	2.253 ms	0 - 32 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	SA8295P ADP	SA8295P	QNN	2.367 ms	0 - 5 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	1.37 ms	0 - 49 MB	INT8	NPU	Person-Foot-Detection-Quantized.tflite
Person-Foot-Detection-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	QNN	1.57 ms	1 - 27 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	1.421 ms	1 - 1 MB	INT8	NPU	Use Export Script
Person-Foot-Detection-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	1.74 ms	8 - 8 MB	INT8	NPU	Person-Foot-Detection-Quantized.onnx

Installation

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

pip install "qai-hub-models[foot_track_net_quantized]"

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.foot_track_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.foot_track_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.foot_track_net_quantized.export

Profiling Results
------------------------------------------------------------
Person-Foot-Detection-Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 1.1                    
Estimated peak memory usage (MB): [0, 1]                 
Total # Ops                     : 146                    
Compute Unit(s)                 : NPU (146 ops)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.foot_track_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.foot_track_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 Person-Foot-Detection-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Person-Foot-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.