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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Personal Protective Equipments
By:
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
Categories
Type
Model Package
Highlights
Maximize workplace safety with real-time monitoring of PPE policy
Harness model data for real-time notifications about PPE policy breaches
Utilize standard security low-resolution video monitoring cameras
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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 PricingWith 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
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": [
{
"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 deployedinput.jpg
- input image to do the inference onimage/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
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Support Information
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.
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