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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.

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Image Segmentation - Mask R-CNN

By:
Inmatura
Latest Version:
1.0.0
Detect and segment objects on images using the state of the art Mask R-CNN model
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    Product Overview

    This model provides object detection on images using a Mask R-CNN (ResNetXt 101 + FPN) architecture. This network provides state of the art accuracy the COCO2017 validation (Box AP: 43.0) and at the same time it provides fast inference times. Supports both CPU and GPU and it features a simple pricing model where you only pay for what you use with a simple metered pricing model. See docs and more info at: https://extrapolations.dev/model/instance-segmentation-mask-r-cnn/

    Key Data

    Version
    1.0.0
    Usage Instructions
    Release Notes
    By
    Inmatura
    Categories
    Object Detection-Image
    Classification-Image
    Image
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Flexible API to detect and classify objects in images with borders

    • State of the Art metric on the COCO validation dataset. Box AP: 43.0

    • Only pay for what you use with a simple metered pricing model

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    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

    Model Realtime Inference$0.10/hr

    running on ml.c5.xlarge

    Model Batch Transform$1.00/hr

    running on ml.c5.xlarge

    Infrastructure 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$0.204/host/hr

    running on ml.c5.xlarge

    SageMaker Batch Transform$0.204/host/hr

    running on ml.c5.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.m5d.24xlarge
    $0.10
    ml.inf1.24xlarge
    $0.10
    ml.m5.2xlarge
    $0.10
    ml.p3.16xlarge
    $0.10
    ml.g4dn.2xlarge
    $0.10
    ml.c5d.4xlarge
    $0.10
    ml.r5.12xlarge
    $0.10
    ml.p3.2xlarge
    $0.10
    ml.inf1.6xlarge
    $0.10
    ml.m5d.large
    $0.10
    ml.g4dn.xlarge
    $0.10
    ml.r5d.24xlarge
    $0.10
    ml.g4dn.16xlarge
    $0.10
    ml.m5d.4xlarge
    $0.10
    ml.m5.xlarge
    $0.10
    ml.c5.9xlarge
    $0.10
    ml.p3.8xlarge
    $0.10
    ml.m5d.12xlarge
    $0.10
    ml.g4dn.8xlarge
    $0.10
    ml.r5.2xlarge
    $0.10
    ml.inf1.2xlarge
    $0.10
    ml.r5d.2xlarge
    $0.10
    ml.g4dn.4xlarge
    $0.10
    ml.m5.4xlarge
    $0.10
    ml.r5d.large
    $0.10
    ml.r5d.12xlarge
    $0.10
    ml.c5.2xlarge
    $0.10
    ml.c5d.9xlarge
    $0.10
    ml.r5.xlarge
    $0.10
    ml.r5d.xlarge
    $0.10
    ml.g4dn.12xlarge
    $0.10
    ml.m5.24xlarge
    $0.10
    ml.m5d.xlarge
    $0.10
    ml.c5.xlarge
    Vendor Recommended
    $0.10
    ml.r5.24xlarge
    $0.10
    ml.p2.xlarge
    $0.10
    ml.m5.12xlarge
    $0.10
    ml.p2.16xlarge
    $0.10
    ml.r5.4xlarge
    $0.10
    ml.c5.4xlarge
    $0.10
    ml.m5d.2xlarge
    $0.10
    ml.c5d.xlarge
    $0.10
    ml.r5d.4xlarge
    $0.10
    ml.p2.8xlarge
    $0.10
    ml.c5.18xlarge
    $0.10
    ml.c5d.18xlarge
    $0.10
    ml.c5d.2xlarge
    $0.10

    Usage Information

    Model input and output details

    Input

    Summary

    The input is one imags in jpg or png format. Returns another image or a JSON object.

    Sample query using the aws CLI:

    aws sagemaker-runtime invoke-endpoint \
        --endpoint-name img-obj-mask-r-cnn \
        --accept application/json \
        --content-type image/jpeg \
        --body fileb://./horse-guard.jpg >(cat)

    For more information and examples on how to use the API see the documentation: https://docs.extrapolations.dev/models/img-obj-mask-r-cnn/api/

    Input MIME type
    image/jpeg, image/png
    Sample input data
    Expand all input descriptions
    Field name
    image
    Description
    The Body of the request should be the image to detect objects on. Supported formats: `jpg` and `.png`.
    Required
    Yes
    Data type
    FreeText
    Field name
    response_type
    Description
    The type of the response. If `jpeg` or `png`, the response content will be an image in the selected format. The image will be the same input image with boxes and labels of the objects detected. If `json` the response will be a JSON object with the labels and coordinates of the objects detected.
    Required
    No
    Data type
    Categorical
    Default value
    jpeg
    Allowed values
    jpeg,png,json
    Usage information

    Output

    Summary

    The output of the model is the same input image with labels and boxes around the detected objects.

    Optionally it can return a JSON object with the metadata of labels and coordinates of the detected objects. The reponse type can be controlled by the Accept or Custom Attributes headers.

    For the complete API docs look at: https://docs.extrapolations.dev/models/img-obj-mask-r-cnn/api/

    Output MIME type
    image/jpeg, image/png, application/json
    Sample output data
    Expand all output descriptions
    Field name
    labels
    Description
    List of labels and probability for the objects detected. Example: ``` ["horse 100%", "person 99%", "person 99%", "person 99%", "person 98%", "person 93%", "person 92%", "frisbee 90%", "umbrella 89%", "person 53%"] ```
    Always returned
    Yes
    Data type
    FreeText
    Field name
    boxes
    Description
    List of coordinate pairs for the object detected. In the same order as the `labels`. Each coordinate pair is in the `[x1, y1, x2, y2]` format. Example: ``` [ [127.11386108398438, 247.31301879882812, 465.427978515625, 480.0], [254.72451782226562, 164.79159545898438, 335.8401184082031, 401.29840087890625], ... ] ```
    Always returned
    Yes
    Data type
    FreeText

    Sample notebook

    Additional Resources

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