Sold by: ProvectusÂ
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Real-time image recognition and classification model to identify personal protective equipment (PPE).
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.
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
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p2.xlarge Inference (Batch) Recommended | Model inference on the ml.p2.xlarge instance type, batch mode | $19.00 |
ml.p2.xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.xlarge instance type, real-time mode | $2.50 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $19.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $19.00 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $19.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $19.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $19.00 |
ml.p3.8xlarge Inference (Real-Time) | Model inference on the ml.p3.8xlarge instance type, real-time mode | $2.50 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $2.50 |
ml.p2.8xlarge Inference (Real-Time) | Model inference on the ml.p2.8xlarge instance type, real-time mode | $2.50 |
Vendor refund policy
We do not offer refunds at this time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
v 1.1.0
Additional details
Inputs
- Summary
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.ipynbÂ
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
- Input MIME type
- image/jpeg
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
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