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PPE-Detection

Object Detection
PyTorch
LiteRT
ONNX
real_time
android
Model card Files Community
1

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's architecture was developed by Qualcomm. The model was trained by Qualcomm on a proprietary dataset, but can be used on any image.

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: Model_use_case.object_detection
  • Model Stats:
    • Inference latency: RealTime
    • Input resolution: 320x192
    • Number of output classes: 2
    • Number of parameters: 6.19M
    • Model size (float): 23.6 MB
    • Model size (w8a8): 6.23 MB
    • Model size (w8a16): 6.65 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
PPE-Detection float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 4.148 ms 0 - 19 MB NPU PPE-Detection.tflite
PPE-Detection float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 4.036 ms 0 - 15 MB NPU PPE-Detection.dlc
PPE-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 1.401 ms 0 - 35 MB NPU PPE-Detection.tflite
PPE-Detection float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 1.566 ms 1 - 25 MB NPU PPE-Detection.dlc
PPE-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.667 ms 0 - 135 MB NPU PPE-Detection.tflite
PPE-Detection float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.656 ms 1 - 74 MB NPU PPE-Detection.dlc
PPE-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.273 ms 0 - 20 MB NPU PPE-Detection.tflite
PPE-Detection float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 1.238 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection float SA7255P ADP Qualcomm® SA7255P TFLITE 4.148 ms 0 - 19 MB NPU PPE-Detection.tflite
PPE-Detection float SA7255P ADP Qualcomm® SA7255P QNN_DLC 4.036 ms 0 - 15 MB NPU PPE-Detection.dlc
PPE-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.669 ms 0 - 134 MB NPU PPE-Detection.tflite
PPE-Detection float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.664 ms 1 - 73 MB NPU PPE-Detection.dlc
PPE-Detection float SA8295P ADP Qualcomm® SA8295P TFLITE 1.81 ms 0 - 19 MB NPU PPE-Detection.tflite
PPE-Detection float SA8295P ADP Qualcomm® SA8295P QNN_DLC 1.764 ms 0 - 18 MB NPU PPE-Detection.dlc
PPE-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.659 ms 0 - 134 MB NPU PPE-Detection.tflite
PPE-Detection float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.664 ms 1 - 73 MB NPU PPE-Detection.dlc
PPE-Detection float SA8775P ADP Qualcomm® SA8775P TFLITE 1.273 ms 0 - 20 MB NPU PPE-Detection.tflite
PPE-Detection float SA8775P ADP Qualcomm® SA8775P QNN_DLC 1.238 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.665 ms 0 - 134 MB NPU PPE-Detection.tflite
PPE-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.663 ms 0 - 72 MB NPU PPE-Detection.dlc
PPE-Detection float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.927 ms 0 - 41 MB NPU PPE-Detection.onnx
PPE-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.485 ms 0 - 34 MB NPU PPE-Detection.tflite
PPE-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.471 ms 1 - 28 MB NPU PPE-Detection.dlc
PPE-Detection float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.643 ms 0 - 31 MB NPU PPE-Detection.onnx
PPE-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.481 ms 0 - 24 MB NPU PPE-Detection.tflite
PPE-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.44 ms 1 - 23 MB NPU PPE-Detection.dlc
PPE-Detection float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.66 ms 1 - 23 MB NPU PPE-Detection.onnx
PPE-Detection float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.768 ms 55 - 55 MB NPU PPE-Detection.dlc
PPE-Detection float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.973 ms 12 - 12 MB NPU PPE-Detection.onnx
PPE-Detection w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 1.422 ms 0 - 15 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.703 ms 0 - 26 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.469 ms 0 - 48 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 0.678 ms 0 - 17 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 2.921 ms 0 - 25 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 SA7255P ADP Qualcomm® SA7255P QNN_DLC 1.422 ms 0 - 15 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.471 ms 0 - 48 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 SA8295P ADP Qualcomm® SA8295P QNN_DLC 1.071 ms 0 - 18 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.465 ms 0 - 48 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 SA8775P ADP Qualcomm® SA8775P QNN_DLC 0.678 ms 0 - 17 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.471 ms 0 - 48 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.676 ms 0 - 47 MB NPU PPE-Detection.onnx
PPE-Detection w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.313 ms 0 - 36 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.43 ms 0 - 35 MB NPU PPE-Detection.onnx
PPE-Detection w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.311 ms 0 - 21 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.462 ms 0 - 24 MB NPU PPE-Detection.onnx
PPE-Detection w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.592 ms 37 - 37 MB NPU PPE-Detection.dlc
PPE-Detection w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.712 ms 6 - 6 MB NPU PPE-Detection.onnx
PPE-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 0.845 ms 0 - 15 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 0.814 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.373 ms 0 - 35 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.485 ms 0 - 26 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.24 ms 0 - 52 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.231 ms 0 - 50 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 0.44 ms 0 - 16 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 0.421 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 1.351 ms 0 - 26 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 1.856 ms 0 - 26 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 4.682 ms 0 - 3 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 0.845 ms 0 - 15 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 0.814 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.248 ms 0 - 52 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.232 ms 0 - 52 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 0.719 ms 0 - 17 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 0.671 ms 0 - 22 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.245 ms 0 - 50 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.236 ms 0 - 51 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 0.44 ms 0 - 16 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 0.421 ms 0 - 16 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.24 ms 0 - 51 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 0.238 ms 0 - 50 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.364 ms 0 - 19 MB NPU PPE-Detection.onnx
PPE-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.18 ms 0 - 37 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.182 ms 0 - 36 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.266 ms 0 - 39 MB NPU PPE-Detection.onnx
PPE-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.203 ms 0 - 18 MB NPU PPE-Detection.tflite
PPE-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 0.178 ms 0 - 21 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.294 ms 0 - 23 MB NPU PPE-Detection.onnx
PPE-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.336 ms 40 - 40 MB NPU PPE-Detection.dlc
PPE-Detection w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.376 ms 6 - 6 MB NPU PPE-Detection.onnx

Installation

Install the 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                          : cs_8275 (ANDROID 14)                
Runtime                         : TFLITE                              
Estimated inference time (ms)   : 4.1                                 
Estimated peak memory usage (MB): [0, 19]                             
Total # Ops                     : 77                                  
Compute Unit(s)                 : npu (77 ops) gpu (0 ops) cpu (0 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 Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

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 --eval-mode 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 -- --eval-mode 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

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