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.
Face Recognition Algorithm
By:
Latest Version:
1.8
This is a trainable algorithm which detects and recognizes faces of individuals on which the model is trained.
Product Overview
Mphasis DeepInsights face recognition algorithm detects the faces present in the image data and uses the concepts of transfer learning to extract high quality features from the facial data known as face embeddings. These face embedding are used to train the machine learning model for face identification.
Key Data
Version
By
Categories
Type
Algorithm
Highlights
Mphasis DeepInsights Face recognition algorithm is a two-step solution. First it identifies the facial features present in the data and then converts them into high quality features know as face embedding. The solution provides the mechanism to train as well as test on user specific data for face identification.
This solution can be used in a variety of applications where facial data may be used as security measures such as access control, social distance monitoring and in location analytics for law enforcement, retail, real estate management, banking and insurance. The other uses of this solution can be unlocking phones, smarter advertising, finding missing persons.
Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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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
Algorithm Training$10/hr
running on ml.m5.4xlarge
Model Realtime Inference$5.00/hr
running on ml.m5.large
Model Batch Transform$10.00/hr
running on ml.m5.large
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 Algorithm Training$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Algorithm Training
For algorithm training 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 | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge Vendor Recommended | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Training
1: The system trains on user provided image dataset. 2: The image dataset should contain folders with name of the person and the corresponding folder should contain facial images of only that person.
** Following are the mandatory inputs for both the APIs:**
• Supported content type for Training API: application/zip
• Supported content type for Testing API: application/json
• The training image dataset should have atleast 15 images of each person
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, text/plain, application/json, text/csv
Compression types: None
Model input and output details
Input
Summary
AWS CLI Command If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://$file_name --content-type application/json --accept application/output.jsonInput MIME type
application/zip, application/json, text/plain, text/csvSample input data
Output
Summary
- Content types: application/json
- Output will be a json array of the properties of persons identified. These properties will include the name of the person, confidence and coordinates of the box enclosing faces present in the image.
Output MIME type
application/json, text/plain, text/csvSample output data
Sample notebook
Additional Resources
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Support Information
Face Recognition Algorithm
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