Sold by: Providence HealthcareÂ
Deployed on AWS
This machine learning model uses computer vision to detect aggregate level of face mask use in waiting rooms within hospital waiting rooms.
Overview
Video-based AI that detects degree of COVID-19 facemask policy compliance. Analyzes statistical patterns at scale.
See also separate Room Occupancy & Social Distance compliance detection module.
Created by Providence Health Care, working with multidisciplinary teams from the University of British Columbia, including the Dept. of Computing Science & Computer Vision Lab. Our mission is to improve patient outcomes, particularly the most vulnerable in our Long-Term Care and Acute Care settings.
Highlights
- Our Digital Products for occupancy counting, social distancing and face mask detection combine video with AI-powered analytics to detect and analyze statistical patterns on how people are complying with policies around max occupancy rates, social distancing and the use of face masks. Providence Health care works closely with multidisciplinary teams at University of British Columbia (UBC), specifically the Department of Computing Science and the Computer Vision Lab. The model is available in public beta subject to further evaluation and training. This model is not intended for medical use.
- This machine learning model uses computer vision to detect aggregate level of face mask use in waiting rooms within hospital waiting rooms. The model is currently in the pilot phase at St. Paul's Hospital in Vancouver, Canada in order to ensure physical distancing and compliance to infection control procedures for the safety of our patients, staff and visitors. The model utilizes SSD (Single Shot Detection) objection detection model and weights by applying a single neural network to the full image. No personally identifiable information is stored as part of the analysis.
- SSD has two components: a backbone model and SSD head. Backbone model is a pre-trained image classification network as a feature extractor. The backbone in this model has 8 conv layers with 1.01M parameters. This model can extract semantic meaning from the input image while preserving the spatial structure of the image albeit at a lower resolution. Input size of the model is 260x260. The total model has only 24 layers with the location and classification layers counted. Code was inspired by https://github.com/AIZOOTech/FaceMaskDetection
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
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.m4.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.2xlarge instance type, real-time mode | $0.00 |
ml.m4.2xlarge Inference (Batch) Recommended | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.c4.2xlarge Inference (Real-Time) | Model inference on the ml.c4.2xlarge instance type, real-time mode | $0.00 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
beta subject to validation and further training
Additional details
Inputs
- Summary
**This model is absolutely not intended as a substitue for a doctor's medical diagnosis. **
Input Supported content types: video
This model accepts videos in the mime-type specified above.
Output Content type: text/plain Please refer to the github notebook for the sample output.
Invoking Endpoint For a single image file: Create an endpoint and provide the source video file in the data directory. The output is obtained in the form of a text file output.txt in the data directory.
- Input MIME type
- video/webm
See Input Summary
See Input Summary
Resources
Support
AWS infrastructure support
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.
Similar products
Image recognition and classification model for real-time PPE compliance monitoring in industrial environments.
Real-time image recognition and classification model to identify personal protective equipment (PPE).
Image recognition and classification model to detect PPE non-compliance in laboratory environments in real-time
Real-time CV model for PPE non-compliance detection in manufacturing, construction, laboratory, and other industrial environments.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.