Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”

Sign in
Your Saved List Become a Channel Partner Sell in AWS Marketplace Amazon Web Services Home Help

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.

product logo

Aluminium Cast Surface Defect Classifier

Latest Version:
1.1
Image analytics-based solution to classify salient surface defects in aluminium die casting.

    Product Overview

    Surface defects in aluminium die casting poses quality and performance risks. Classifying defects enables for the rapid identification and removal of the causes of their occurrence, as well as the provision of appropriate treatment to fix them. This Deep Learning-based solution identifies three classes of salient surface defects: blowhole, porosity and shrinkage cavity. This solution analyses the user provided image data, identifies the best performing deep learning model architecture, and predicts the defect class with the highest probability score. This can assist metal products manufacturing companies to improve their quality control process.

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • This solution identifies three classes of salient surface defects in aluminium die casting: blowhole, porosity and shrinkage cavity. This solution can assist metal products manufacturing companies to improve their quality control process.

    • This solution analyses flat surface images of aluminium die casting and identifies the best performing deep learning model architecture for defect classification. This solution improves the turnaround time for developing AI-powered visual inspection systems.

    • Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!

    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

    Algorithm Training$10/hr

    running on ml.m5.4xlarge

    Model Realtime Inference$10.00/hr

    running on ml.m5.12xlarge

    Model Batch Transform$20.00/hr

    running on ml.m5.2xlarge

    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 Algorithm Training$0.922/host/hr

    running on ml.m5.4xlarge

    SageMaker Realtime Inference$2.765/host/hr

    running on ml.m5.12xlarge

    SageMaker Batch Transform$0.461/host/hr

    running on ml.m5.2xlarge

    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

    • The training input should be a ZIP file of images. It must have images classified into separate folders based on the respective defect types as explained in the Sample Input document.
    • Each input image must adhere to the minimum size limits: Height 200 px, Width 200 px.
    • Images must be in PNG or JPG formats.
    • For optimal results, images must have minimal background noise.
    • The hyperparameter details (max_try, no_epochs) must be provided in the Jupyter notebook.
    • For detailed instructions, please refer sample Jupyter notebook.

    Channel specification

    Fields marked with * are required

    training

    *
    Input modes: File
    Content types: application/zip, text/plain, application/json, text/csv
    Compression types: None

    Hyperparameters

    Fields marked with * are required

    no_epochs

    *
    Number of Epoches to run in training
    Type: Integer
    Tunable: No

    max_try

    *
    The maximum number of different Keras Models to try. The search may finish before reaching the max_trials.
    Type: Integer
    Tunable: No

    Model input and output details

    Input

    Summary

    This algorithm takes ZIP file as input. The ZIP file to be uploaded for testing must have images that are not classified.

    Input MIME type
    application/zip
    Sample input data

    Output

    Summary

    The output will be a CSV file with filenames of images from the testing ZIP file and the defect type with the highest probability score. An illustrative example is provided in the Sample Output document.

    Output MIME type
    application/zip
    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

    Aluminium Cast Surface Defect Classifier

    For any assistance reach out to us at:

    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.

    Learn More

    Refund Policy

    Currently we do not support refunds, but you can cancel your subscription to the service at any time.

    Customer Reviews

    There are currently no reviews for this product.
    View all