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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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SSD Mobilenet V2 320x320

By:
Amazon Web Services
Latest Version:
GPU
This is a Object Detection Answering model from TensorFlow Hub
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    Product Overview

    This is an object detection model from [TensorFlow Hub](https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1 ). It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task is to identify the objects and their positions in the image. A list of the objects that the model can identify is given at the end of the page. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

    Key Data

    Version
    GPU
    Usage Instructions
    Release Notes
    Show other versions
    By
    Amazon Web Services
    Categories
    Computer Vision
    Object Detection-Image
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

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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.g4dn.xlarge

    Model Batch Transform$0.00/hr

    running on ml.p2.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$0.736/host/hr

    running on ml.g4dn.xlarge

    SageMaker Batch Transform$1.125/host/hr

    running on ml.p2.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.g4dn.xlarge
    Vendor Recommended
    $0.00
    ml.p2.xlarge
    $0.00
    ml.p3.2xlarge
    $0.00

    Usage Information

    Model input and output details

    Input

    Summary

    The input is an image.

    Input MIME type
    application/x-image
    Sample input data

    Output

    Summary

    The output is a JSON object corresponding to the TensorFlow Object Detection API output format.

    Output MIME type
    application/json
    Sample output data
    Expand all output descriptions
    Field name
    detection_multiclass_scores
    Description
    Class score distribution (including background) for detection boxes in the image including background class.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Field name
    detection_classes
    Description
    Detection-level class labels.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Field name
    num_detections
    Description
    Number of detections in the batch.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Field name
    detection_boxes
    Description
    Coordinates of the detection boxes in the image.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0.0
    Max: 1.0
    Field name
    detection_scores
    Description
    Detection scores for the detection boxes in the image.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range
    Min: 0.0
    Max: 1.0
    Field name
    detection_anchor_indices
    Description
    The anchor indices of the detections after NMS.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range

    Sample notebook

    Additional Resources

    End User License Agreement

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    Support Information

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    Refund Policy

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