Sold by: Mphasis
Deployed on AWS
Image analytics-based solution to classify surface defects in sheet steel.
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.
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.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $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.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge 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 model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
Updated Version.
Additional details
Inputs
- 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/zip
https://github.com/Mphasis-ML-Marketplace/Steel-Surface-Defect-Classifier/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Steel-Surface-Defect-Classifier/tree/main/Input
Resources
Vendor resources
Support
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