
Overview
This model identifies if a given parking slot is occupied or not. It is trained using convolutional neural network (CNN) on parking lot images to identify occupancy. This model can be extended as edge ML model with a parking monitoring drone that continuously monitors available and occupied parking lots.
Highlights
- A binary classifier trained using convolutional neural network (CNN) that processes parking lot images and classifies them as occupied or available
- Trained using SageMaker default Image Classification algorithm
- An edge ML based solution can be extended using this model and parking lot monitoring drone
Details
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Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.00 |
Vendor refund policy
Since you are not being charged currently for the use of this software there will be no refund of any charges.
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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.
Version release notes
First version released to AWS ML Marketplace
Additional details
Inputs
- Summary
Download the Jupyter notebook in "Additional Resources" section and follow readme.txt provided.
- Input MIME type
- image/jpeg
Resources
Vendor resources
Support
AWS infrastructure support
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