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.
Steel Surface Defects Classifier
By:
Latest Version:
2.2
Image analytics-based solution to classify surface defects in sheet steel.
Product Overview
Surface defects in sheet steel poses quality and performance risks. Classifying various defects allows to quickly identify and remove the causes of their occurrence. This Transfer Learning-based solution identifies three classes of surface defects: holes, peels, and others (cracks, scratches, etc.). This solution analyses the input image and provides probability scores for the three defect classes. This can assist metal products manufacturing companies to improve their quality control process.
Key Data
Version
By
Type
Model Package
Highlights
This solution identifies three classes of surface defects in sheet steel: holes, peels, and others (cracks, scratches, etc). This solution can assist metal products manufacturing companies to improve their quality control process.
This solution takes in flat surface images of the sheet steel and uses CNN based model to identify and classify any defects on the surface. This solution can enable high speed and accurate quality check for the processing plant.
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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.
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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$10.00/hr
running on ml.m5.large
Model Batch Transform$20.00/hr
running on ml.m5.large
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 Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
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 | $10.00 | |
ml.m5.4xlarge | $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 Vendor Recommended | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Model input and output details
Input
Summary
- The input should be a zip file of images.
- Each input image must adhere to the minimum size limits: Height 200 px, Width 200 px.
- For optimal results, images must have minimal background noise.
Input MIME type
application/zipSample input data
Output
Summary
- The output will be a CSV file with filenames of images and probability scores for the three defect classes. Each value corresponds to each defect class.
- First value corresponds to holes, second value corresponds to scratches, cracks and others and final value corresponds to peels.
Output MIME type
text/csv, text/plainSample output data
Sample notebook
Additional Resources
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Support Information
Steel Surface Defects Classifier
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