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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.

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Construction Worker Detection

By:
Latest Version:
v1
The model identifies unauthorized access to the construction site.

    Product Overview

    The model identifies construction workers by the protective equipment (such as hardhats or high visibility vests) worn. If the model identifies a person without protective equipment, it identifies the action as unauthorized access to the construction site.

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Maximize security by tracking unauthorized access to the construction site

    • Differentiate between different types of personnel based on the uniforms worn

    • Use standard set of security video surveliance cameras

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$4.00/hr

    running on ml.c4.8xlarge

    Model Batch Transform$4.00/hr

    running on ml.c4.8xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$1.909/host/hr

    running on ml.c4.8xlarge

    SageMaker Batch Transform$1.909/host/hr

    running on ml.c4.8xlarge

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.m4.4xlarge
    $4.00
    ml.m5.4xlarge
    $4.00
    ml.m4.16xlarge
    $4.00
    ml.m5.2xlarge
    $4.00
    ml.p3.16xlarge
    $4.00
    ml.m4.2xlarge
    $4.00
    ml.c5.2xlarge
    $4.00
    ml.p3.2xlarge
    $4.00
    ml.c4.2xlarge
    $4.00
    ml.m4.10xlarge
    $4.00
    ml.c4.xlarge
    $4.00
    ml.m5.24xlarge
    $4.00
    ml.c5.xlarge
    $4.00
    ml.p2.xlarge
    $4.00
    ml.m5.12xlarge
    $4.00
    ml.p2.16xlarge
    $4.00
    ml.c4.4xlarge
    $4.00
    ml.m5.xlarge
    $4.00
    ml.c5.9xlarge
    $4.00
    ml.m4.xlarge
    $4.00
    ml.c5.4xlarge
    $4.00
    ml.p3.8xlarge
    $4.00
    ml.m5.large
    $4.00
    ml.c4.8xlarge
    Vendor Recommended
    $4.00
    ml.p2.8xlarge
    $4.00
    ml.c5.18xlarge
    $4.00

    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":[
          {
             "bbox":[
                168,
                -8,
                412,
                880         
    ],
             "class":"worker"
    },
          {
             "bbox":[
                482,
                52,
                842,
                833   
    ],
             "class":"person"      
    } 
    ]
    }

    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('path-to-img', '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/

    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

    Construction Worker Detection

    AWS Infrastructure

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Learn More

    Refund Policy

    We do not offer refunds at this time

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