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Person-Foot-Detection

Object Detection
PyTorch
TF Lite
ONNX
real_time
android
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metadata
datasets:
  - coco
library_name: pytorch
license: bsd-3-clause
pipeline_tag: object-detection
tags:
  - real_time
  - android

Person-Foot-Detection: Optimized for Mobile Deployment

Multi-task Human detector

FootTrackNet can detect person and face bounding boxes, head and feet landmark locations and feet visibility.

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 3.484 ms 0 - 25 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 3.592 ms 4 - 12 MB FP16 NPU Person-Foot-Detection.so
Person-Foot-Detection Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 5.293 ms 15 - 18 MB FP16 NPU Person-Foot-Detection.onnx
Person-Foot-Detection Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 2.884 ms 0 - 55 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 3.046 ms 4 - 21 MB FP16 NPU Person-Foot-Detection.so
Person-Foot-Detection Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 4.623 ms 0 - 66 MB FP16 NPU Person-Foot-Detection.onnx
Person-Foot-Detection QCS8550 (Proxy) QCS8550 Proxy TFLITE 3.339 ms 0 - 2 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection QCS8550 (Proxy) QCS8550 Proxy QNN 3.293 ms 2 - 3 MB FP16 NPU Use Export Script
Person-Foot-Detection SA8255 (Proxy) SA8255P Proxy TFLITE 3.382 ms 0 - 109 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection SA8255 (Proxy) SA8255P Proxy QNN 3.365 ms 3 - 4 MB FP16 NPU Use Export Script
Person-Foot-Detection SA8775 (Proxy) SA8775P Proxy TFLITE 3.443 ms 0 - 41 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection SA8775 (Proxy) SA8775P Proxy QNN 3.387 ms 2 - 3 MB FP16 NPU Use Export Script
Person-Foot-Detection SA8650 (Proxy) SA8650P Proxy TFLITE 3.504 ms 0 - 4 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection SA8650 (Proxy) SA8650P Proxy QNN 3.384 ms 4 - 5 MB FP16 NPU Use Export Script
Person-Foot-Detection QCS8450 (Proxy) QCS8450 Proxy TFLITE 5.66 ms 5 - 58 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection QCS8450 (Proxy) QCS8450 Proxy QNN 5.772 ms 4 - 25 MB FP16 NPU Use Export Script
Person-Foot-Detection Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 2.376 ms 0 - 29 MB FP16 NPU Person-Foot-Detection.tflite
Person-Foot-Detection Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 2.491 ms 0 - 17 MB FP16 NPU Use Export Script
Person-Foot-Detection Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 3.68 ms 17 - 51 MB FP16 NPU Person-Foot-Detection.onnx
Person-Foot-Detection Snapdragon X Elite CRD Snapdragon® X Elite QNN 3.669 ms 4 - 4 MB FP16 NPU Use Export Script
Person-Foot-Detection Snapdragon X Elite CRD Snapdragon® X Elite ONNX 5.762 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)   : 3.5                    
Estimated peak memory usage (MB): [0, 25]                
Total # Ops                     : 134                    
Compute Unit(s)                 : NPU (134 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