Sold by: Appen (fka Figure Eight)Â
Overview
This model blurs the faces of people in an image to preserve privacy. It was trained on the “Labeled Faces in the Wild” dataset and tested on small and medium sized images. This is a great tool to mitigate privacy concerns when showing images which contain people in a public setting.
Please note: though this model currently is optimized for caucasian men based solely on the training data set coverage, it will also work for other ethnicities and genders. A deep dive on the training data set to better understand the limitations is recommended.
Highlights
- This model blurs the faces of people in an image to preserve privacy.
Details
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Delivery method
Latest version
Deployed on AWS
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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.c5.xlarge Inference (Batch) Recommended | Model inference on the ml.c5.xlarge instance type, batch mode | $0.45 |
ml.c5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.45 |
Vendor refund policy
We do not currently support refunds, but you can cancel at any 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
For CPU: We recommend using a ml.c5.xlarge (CPU) instance type. Our tests on these took 6.8 seconds prediction time for average payloads of 22 KB when invoked from a desktop.
Additional details
Inputs
- Summary
Example for the /invocations endpoint:
Input (application/json): Image with peoples faces that needs to be blurred [base64 encoded] Payload: {"instances": [{"image": {"b64": "BASE_64_ENCODED_IMAGE_CONTENTS"}}]}
Output (application/json): Image with the predicted faces blurred.
Content: {"predictions": [ { "image": {"b64", "a35..."}}]}
- Input MIME type
- json
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
If you need any support, have questions, or suggestions, please email ml-support@figure-eight.comÂ
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.
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