Overview
This solution is an innovative machine learning model on the AWS that leverages the power of quantum computing for real-time damaged vehicle image classification. This cutting-edge product utilizes a complex algorithm to accurately assess the severity of vehicle damage and classify its level, providing valuable insights for insurance companies and car manufacturers. With the advanced capabilities of quantum computing, our solution offers faster and more efficient image classification, revolutionizing the way we make informed decisions about vehicle repair and replacement.
Highlights
- The Quantum Simulator-based Damaged Vehicle Classifier uses advanced quantum computing algorithms to accurately assess the extent of vehicle damage in real-time. This enables insurance companies and car manufacturers to make data-driven decisions about repair or replacement, ultimately streamlining the claims process and improving customer satisfaction.
- By harnessing the power of quantum computing, our damaged vehicle classifier delivers faster and more efficient image classification compared to traditional classical computing methods. This high-speed, real-time assessment allows stakeholders to make informed decisions quickly, resulting in cost savings and enhanced operational efficiency. Experience the transformative power of quantum computing in vehicle damage classification with our innovative machine learning model.
- Need more machine learning, deep learning, NLP and Quantum Computing solutions. Reach out to us at Harman DTS.
Details
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $150.00 |
ml.t2.xlarge Inference (Real-Time) Recommended | Model inference on the ml.t2.xlarge instance type, real-time mode | $30.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $150.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $150.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $150.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $150.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $150.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $150.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $150.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $150.00 |
Vendor refund policy
We do not provide any usage related refunds at this time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Bug fixes and feature updates
Additional details
Inputs
- Summary
The input is a jpg or png image of a automobile
- Input MIME type
- image/jpeg, image/jpg, image/png
https://github.com/HDTS-user/Quantum-Damaged-Vehicle-Classification/tree/main/input
https://github.com/HDTS-user/Quantum-Damaged-Vehicle-Classification/tree/main/input
Resources
Vendor resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
This solution analyzes images of civil structure surfaces and predicts whether they have cracks or not, using quantum ML.
A hybrid quantum algorithm-based solution that forecasts the remaining useful life of a system using IOT sensors data to predict failures
Quantum Emulator based vehicle damage classifier is a Hybrid QML image classifier designed to detect damaged vehicle images.
Quantum Emulator based damaged shipment classifier is a Hybrid QML image classifier designed to detect damaged shipment images.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.