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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Hard Hat Detector for Worker Safety Free trial
By:
Latest Version:
1.1.0
Real-time image recognition and classification model to identify personal protective equipment (PPE).
Product Overview
The Hard Hat Detector for Industrial Worker Safety - is a computer vision-driven ML model designed to detect PPE compliance/non-compliance on the factory floor or at the construction site in real time. It analyzes image footage, identifies workers, and checks if they follow safety regulations, giving you the bounding box coordinates and class of each worker. Trained on data of the CCTV footage collected at the production plant, this ML model can be used in oil & gas, manufacturing, construction, and steelmaking industry to ensure safety compliance.
Key Data
Version
Type
Model Package
Highlights
Accurate detection of workers’ PPE compliance or non-compliance in an image.
Results are aggregated in JSON format for convenient use in dev/production.
Need a custom-made solution for video/image analysis? Reach us at hello@provectus.com
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$2.50/hr
running on ml.p2.xlarge
Model Batch Transform$19.00/hr
running on ml.p2.xlarge
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$1.125/host/hr
running on ml.p2.xlarge
SageMaker Batch Transform$1.125/host/hr
running on ml.p2.xlarge
About Free trial
Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
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.p2.xlarge Vendor Recommended | $2.50 | |
ml.p3.8xlarge | $2.50 | |
ml.p3.2xlarge | $2.50 | |
ml.p2.8xlarge | $2.50 | |
ml.p2.16xlarge | $2.50 | |
ml.p3.16xlarge | $2.50 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Usage Instructions: Supported content types are ["image/jpeg"]
Supported response types are "application/json"
After creating an endpoint, you can use any AWS Sagemaker APIs to use the model.
The easiest way is with our supplied Jupyter Notebook: https://github.com/provectus/ai-worker-safety-notebooks/blob/master/endpoint_usage_example.ipynb
But you can also use the AWS CLI: aws sagemaker-runtime invoke-endpoint --endpoint-name "your_endpoint" --body fileb://test_image.jpeg --content-type "image/jpeg" output.json
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
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 MoreRefund Policy
We do not offer refunds at this time.
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