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Amazon SageMaker ×

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Personal Protective Equipments

By: Agmis Latest Version: V1

The model identifies whether required personal protection equipment (PPE) is worn by the workers.

Product Overview

The model recognizes PPE equipment, including high-visibility vests, hardhats of a pre-assigned area (such as a construction site, cargo loading area, factory floor etc.) The model is used to ensure complience with company safety protocol and identify safety breaches. Data provided by the model can be utilized to notify safety officer about PPE protocol breaches in real-time or used for safety procedure breaches and analysis at a later stage.

Key Data

Version
By Agmis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

Supported content types: image/jpeg, image/png, application/x-image

Output

Content type: application/json Sample output:

{
    "output": [
        {
            "score": 0.9997282028198242,
            "bbox": [
                485,
                -31,
                1119,
                1312
            ],
            "ppe": {
                "helmet": 0.715499997138977,
                "vest": 0.0017999999690800905
            }
        }
    ]
}

Invoking endpoint

AWS CLI Command

You can invoke endpoint using AWS CLI:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.jpg --content-type image/jpeg --accept application/json out.json

Substitute the following parameters:

  • "endpoint-name" - name of the inference endpoint where the model is deployed
  • input.jpg - input image to do the inference on
  • image/jpeg - MIME type of the given input image (above)
  • out.json - filename where the inference results are written to

Python

Real-time inference snippet (more detailed example can be found in sample notebook):

runtime = boto3.Session().client(service_name='runtime.sagemaker')
bytes_image = open('input.jpg', 'rb').read()
response = runtime.invoke_endpoint(EndpointName='endpoint-name', ContentType='image/jpeg', Body=bytes_image)
response = response['Body'].read()
results = json.loads(response)

Resources

If you have any questions feel free to contact us info@agmis.eu or fill our https://agmis.lt/contacts/

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

Personal Protective Equipments

Email.: info@agmis.eu

https://agmis.lt/contact-us/

AWS Infrastructure

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Refund Policy

We do not offer refunds at this time

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