PPE-Detection: Optimized for Mobile Deployment

Object detection for personal protective equipments (PPE)

Detect if a person is wearing personal protective equipments (PPE) in real-time.

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

This repository provides scripts to run PPE-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: 320x192
  • Number of parameters: 7.02M
  • Model size: 13.5 MB
  • Number of output classes: 2

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
PPE-Detection	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	0.67 ms	0 - 232 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	0.725 ms	0 - 49 MB	FP16	NPU	PPE-Detection.so
PPE-Detection	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	1.105 ms	0 - 15 MB	FP16	NPU	PPE-Detection.onnx
PPE-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	0.561 ms	0 - 42 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.613 ms	1 - 16 MB	FP16	NPU	PPE-Detection.so
PPE-Detection	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	0.943 ms	0 - 46 MB	FP16	NPU	PPE-Detection.onnx
PPE-Detection	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	0.666 ms	0 - 4 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	QCS8550 (Proxy)	QCS8550 Proxy	QNN	0.722 ms	1 - 2 MB	FP16	NPU	Use Export Script
PPE-Detection	SA8255 (Proxy)	SA8255P Proxy	TFLITE	0.671 ms	0 - 242 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	SA8255 (Proxy)	SA8255P Proxy	QNN	0.728 ms	1 - 2 MB	FP16	NPU	Use Export Script
PPE-Detection	SA8775 (Proxy)	SA8775P Proxy	TFLITE	0.669 ms	0 - 174 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	SA8775 (Proxy)	SA8775P Proxy	QNN	0.731 ms	1 - 2 MB	FP16	NPU	Use Export Script
PPE-Detection	SA8650 (Proxy)	SA8650P Proxy	TFLITE	0.666 ms	0 - 7 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	SA8650 (Proxy)	SA8650P Proxy	QNN	0.73 ms	1 - 2 MB	FP16	NPU	Use Export Script
PPE-Detection	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	1.428 ms	0 - 39 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	QCS8450 (Proxy)	QCS8450 Proxy	QNN	1.492 ms	1 - 16 MB	FP16	NPU	Use Export Script
PPE-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	0.48 ms	0 - 22 MB	FP16	NPU	PPE-Detection.tflite
PPE-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.44 ms	0 - 14 MB	FP16	NPU	Use Export Script
PPE-Detection	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	0.713 ms	0 - 24 MB	FP16	NPU	PPE-Detection.onnx
PPE-Detection	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	0.853 ms	1 - 1 MB	FP16	NPU	Use Export Script
PPE-Detection	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	1.173 ms	13 - 13 MB	FP16	NPU	PPE-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.gear_guard_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.gear_guard_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.gear_guard_net.export

Profiling Results
------------------------------------------------------------
PPE-Detection
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 0.7                    
Estimated peak memory usage (MB): [0, 232]               
Total # Ops                     : 80                     
Compute Unit(s)                 : NPU (80 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.gear_guard_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.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.gear_guard_net.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.gear_guard_net.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 PPE-Detection's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

• The license for the original implementation of PPE-Detection can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• None
• Source Model Implementation

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.