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.
GluonCV DeepLab Semantic Segmentation
Latest Version:
1.0
DeepLab is a powerful model for image semantic segmentation, powered by GluonCV.
Product Overview
Given an input image, this model will generate a mask that describes the category for each pixel. For training new models on your own datasets, the algorithm is also available as built-in SageMaker algorithm: https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html
Key Data
Version
Type
Model Package
Highlights
This model will generate a 2D integer mask to describe the class of each pixel.
Provides state-of-the-art segmentation performance with 86.7 pixel accuracy vs 85.7 in the original paper (https://arxiv.org/abs/1706.05587 )
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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.
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$0.00/hr
running on ml.c5.4xlarge
Model Batch Transform$0.00/hr
running on ml.c4.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$0.816/host/hr
running on ml.c5.4xlarge
SageMaker Batch Transform$0.239/host/hr
running on ml.c4.xlarge
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.m4.4xlarge | $0.00 | |
ml.m5.4xlarge | $0.00 | |
ml.m5.12xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 | |
ml.m5.2xlarge | $0.00 | |
ml.c4.4xlarge | $0.00 | |
ml.m5.xlarge | $0.00 | |
ml.c5.9xlarge | $0.00 | |
ml.m4.xlarge | $0.00 | |
ml.c5.4xlarge Vendor Recommended | $0.00 | |
ml.m4.2xlarge | $0.00 | |
ml.c5.2xlarge | $0.00 | |
ml.m5.large | $0.00 | |
ml.c4.2xlarge | $0.00 | |
ml.c4.8xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.c4.xlarge | $0.00 | |
ml.m5.24xlarge | $0.00 | |
ml.c5.18xlarge | $0.00 | |
ml.c5.xlarge | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Supported content types are image/jpeg, image/png and image/bmp.
AWS APIs can be used to invoke the model after endpoint creation, for example, using aws-cli:
aws sagemaker-runtime invoke-endpoint --endpoint-name your_endpoint_name --body fileb://img.jpg --content-type image/jpeg --accept json mask.out
Additional Resources
End User License Agreement
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Support Information
GluonCV DeepLab Semantic Segmentation
Model supported is available from GluonCV. Search for questions and open new issues to ask questions.
AWS Infrastructure
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Learn MoreRefund Policy
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