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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 Recognition with PyTorch Resnet

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Latest Version:
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Image Recognition and Classification with PyTorch Vision Resnet | Rearc

    Product Overview

    This product is an Image Recognition and Classification model from PyTorch Hub. It takes an image as input and classifies the image into one of the multiple labels. How was the model trained? The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The ImageNet project is a large visual database designed for use in visual object recognition software research. Do you want to create an Image Classification Web or Mobile app? Look no further and subscribe to this model to get started! Please don't hesitate in reaching out to us by clicking: "Contact Seller" above. We are excited to get to know your use-cases and help you out by providing training data, model customizations, and more!

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    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.


    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.m4.xlarge

    Model Batch Transform$0.00/hr

    running on ml.m4.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$0.24/host/hr

    running on ml.m4.xlarge

    SageMaker Batch Transform$0.24/host/hr

    running on ml.m4.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.m4.16xlarge
    $0.00
    ml.m5.2xlarge
    $0.00
    ml.p3.16xlarge
    $0.00
    ml.m4.2xlarge
    $0.00
    ml.c5.2xlarge
    $0.00
    ml.p3.2xlarge
    $0.00
    ml.c4.2xlarge
    $0.00
    ml.m4.10xlarge
    $0.00
    ml.c4.xlarge
    $0.00
    ml.m5.24xlarge
    $0.00
    ml.c5.xlarge
    $0.00
    ml.p2.xlarge
    $0.00
    ml.m5.12xlarge
    $0.00
    ml.p2.16xlarge
    $0.00
    ml.c4.4xlarge
    $0.00
    ml.m5.xlarge
    $0.00
    ml.c5.9xlarge
    $0.00
    ml.m4.xlarge
    Vendor Recommended
    $0.00
    ml.c5.4xlarge
    $0.00
    ml.p3.8xlarge
    $0.00
    ml.m5.large
    $0.00
    ml.c4.8xlarge
    $0.00
    ml.p2.8xlarge
    $0.00
    ml.c5.18xlarge
    $0.00

    Usage Information

    Model input and output details

    Input

    Summary

    Input Data

    Images to Classify mime types:

    • application/json

    Example: {'image': '<binary of image>'}

    Sample Snippet:

    import base64
    import json
    
    image_file = 'cat.jpg'
    with open(image_file, "rb") as f:
        im_bytes = f.read()
    
    im_b64 = base64.b64encode(im_bytes).decode("utf8")
    predictor.predict({"image": im_b64})
    Input MIME type
    application/json
    Sample input data

    Output

    Summary

    Image Recognition Output: The top-5 prediction labels of the input image and its probability.

    Sample

    ``` { "tiger cat": 64.559, "tabby, tabby cat": 28.44, "Egyptian cat": 5.997, "lynx, catamount": 0.464, "tiger, Panthera tigris": 0.122 } ``

    Output MIME type
    application/json
    Sample output data

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Image Recognition with PyTorch Resnet

    Please feel free to reach out to us at: data@rearc.io (or by clicking "Contact Seller")

    AWS Infrastructure

    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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    Refund Policy

    This product is offered for free. If there are any questions, please contact us for further clarifications.

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