Person-Foot-Detection: 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 found here.

This repository provides scripts to run Person-Foot-Detection on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

Model Type: Object detection
Model Stats:
- 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	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	4.944 ms	5 - 6 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	5.062 ms	4 - 11 MB	FP16	NPU	Person-Foot-Detection.so
Person-Foot-Detection	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	5.472 ms	14 - 18 MB	FP16	NPU	Person-Foot-Detection.onnx
Person-Foot-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	3.443 ms	0 - 58 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	3.564 ms	0 - 21 MB	FP16	NPU	Person-Foot-Detection.so
Person-Foot-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	4.096 ms	20 - 88 MB	FP16	NPU	Person-Foot-Detection.onnx
Person-Foot-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	3.601 ms	0 - 29 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	3.635 ms	0 - 18 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	3.869 ms	19 - 54 MB	FP16	NPU	Person-Foot-Detection.onnx
Person-Foot-Detection	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	4.893 ms	5 - 43 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	QCS8550 (Proxy)	QCS8550 Proxy	QNN	4.778 ms	4 - 5 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	SA8255 (Proxy)	SA8255P Proxy	TFLITE	5.038 ms	5 - 56 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	SA8255 (Proxy)	SA8255P Proxy	QNN	4.845 ms	4 - 8 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	SA8775 (Proxy)	SA8775P Proxy	TFLITE	4.95 ms	5 - 127 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	SA8775 (Proxy)	SA8775P Proxy	QNN	4.914 ms	4 - 5 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	SA8650 (Proxy)	SA8650P Proxy	TFLITE	5.01 ms	4 - 7 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	SA8650 (Proxy)	SA8650P Proxy	QNN	4.948 ms	4 - 6 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	SA8295P ADP	SA8295P	TFLITE	8.458 ms	5 - 32 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	SA8295P ADP	SA8295P	QNN	8.814 ms	0 - 6 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	7.095 ms	5 - 60 MB	FP16	NPU	Person-Foot-Detection.tflite
Person-Foot-Detection	QCS8450 (Proxy)	QCS8450 Proxy	QNN	7.526 ms	4 - 28 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	5.363 ms	4 - 4 MB	FP16	NPU	Use Export Script
Person-Foot-Detection	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	5.875 ms	17 - 17 MB	FP16	NPU	Person-Foot-Detection.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.foot_track_net.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.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.export

Profiling Results
------------------------------------------------------------
Person-Foot-Detection
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 4.9                    
Estimated peak memory usage (MB): [5, 6]                 
Total # Ops                     : 135                    
Compute Unit(s)                 : NPU (135 ops)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.foot_track_net import 

# Load the model

# Device
device = hub.Device("Samsung Galaxy S23")

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

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'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 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.