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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Aluminium Cast Surface Defect Classifier
By:
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
Version
By
Type
Algorithm
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.
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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.
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 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 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/zipSample 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/zipSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Aluminium Cast Surface Defect Classifier
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