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.
QML based Infrastructure Crack Detection
By:
Latest Version:
2.1
This solution analyzes images of civil structure surfaces and predicts whether they have cracks or not, using quantum ML.
Product Overview
This is a hybrid classical-quantum machine learning based solution which detects cracks on concrete and other civil infrastructure surface images. The algorithm uses a pre-trained DCGAN before variational quantum circuit. The DCGAN model is trained on in hand dataset to perform feature transformation. This trained DCGAN model enhances feature to be used as input in quantum architecture. The algorithm used in this solution inherits variational quantum circuit layers with trained parameters dedicated for surface crack image classification.
Key Data
Version
By
Type
Model Package
Highlights
The appearance of cracks and distortions can be visually unattractive and disconcerting for occupants, and if left untreated they can affect the integrity, safety and stability of the structure. In case of railway bridge, flyover or foot bridge, it is crucial to regularly inspect the structures for cracks or any other defects. this solution can be used by agencies like Municipalities, review boards, construction comapanies to moniter the civil structure health and take corrective action when necessary.
Quantum based Surface Crack Detection solution analyzes the images of concrete surfaces and predicts presence or absence of cracks. The current solution provides quantum ML based alternative to state of the art classifical deep learning based image classification 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
Model Realtime Inference$20.00/hr
running on ml.m5.large
Model Batch Transform$40.00/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 | $20.00 | |
ml.m5.4xlarge | $20.00 | |
ml.m4.16xlarge | $20.00 | |
ml.m5.2xlarge | $20.00 | |
ml.p3.16xlarge | $20.00 | |
ml.m4.2xlarge | $20.00 | |
ml.c5.2xlarge | $20.00 | |
ml.p3.2xlarge | $20.00 | |
ml.c4.2xlarge | $20.00 | |
ml.m4.10xlarge | $20.00 | |
ml.c4.xlarge | $20.00 | |
ml.m5.24xlarge | $20.00 | |
ml.c5.xlarge | $20.00 | |
ml.p2.xlarge | $20.00 | |
ml.m5.12xlarge | $20.00 | |
ml.p2.16xlarge | $20.00 | |
ml.c4.4xlarge | $20.00 | |
ml.m5.xlarge | $20.00 | |
ml.c5.9xlarge | $20.00 | |
ml.m4.xlarge | $20.00 | |
ml.c5.4xlarge | $20.00 | |
ml.p3.8xlarge | $20.00 | |
ml.m5.large Vendor Recommended | $20.00 | |
ml.c4.8xlarge | $20.00 | |
ml.p2.8xlarge | $20.00 | |
ml.c5.18xlarge | $20.00 |
Usage Information
Model input and output details
Input
Summary
- The input dataset should be a zip folder containing images in png format.
- Input zip folder should not contain more than 5 images.
Input MIME type
application/zipSample input data
Output
Summary
The output file (in csv format) contains the following columns:
- 'file_name': List of filenames.
- 'prediction': Corresponding predicted label (surface cracked or not).
Output MIME type
text/csvSample output data
Sample notebook
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
QML based Infrastructure Crack Detection
For any assistance reach out to us at:
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