Sold by: MphasisÂ
Deployed on AWS
Image analytics-based solution to classify salient surface defects in steel strip.
Overview
Surface defects in hot rolled steel strip 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 four classes of salient surface defects: slag inclusion, oxide scale, scratches, and iron sheet ash. 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.
Highlights
- This solution identifies four classes of salient surface defects in steel strip: slag inclusion, oxide scale, scratches, and iron sheet ash. This solution can assist metal products manufacturing companies to improve their quality control process.
- This solution analyses flat surface images of steel strip 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!
Details
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Delivery method
Latest version
Deployed on AWS
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.m5.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.12xlarge instance type, real-time mode | $10.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the version 1.1 of the algorithm.
Additional details
Inputs
- 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
https://github.com/Mphasis-ML-Marketplace/strip-steel-defect-classifier/tree/main/Sample%20Input
https://github.com/Mphasis-ML-Marketplace/strip-steel-defect-classifier/tree/main/Sample%20Input
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Vendor resources
Support
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