Sold by: Persistent SystemsÂ
Overview
The classification of the galaxy is vital for studying the formation of galaxies and evaluation of our universe. Galaxy morphological classification is a framework (devised by Edwin Hubble) that divides galaxies into groups based on their visual appearance such as elliptical galaxies, spiral galaxies and irregular ones.
Furthermore, spiral galaxies are divided in ordinary and barred. This model uses convolutional neural network (CNN) to classify images of spiral galaxies as barred and unbarred galaxy.
Highlights
- A binary classifier trained using convolutional neural network (CNN) that processes the spiral galaxy images and classifies them as barred and unbarred
- This model has been trained using open dataset of galaxy images provided, labelled and validated by domain experts from Inter-University Centre for Astronomy and Astrophysics(IUCAA), India, Pune (https://www.iucaa.in/).
- Acknowledgment: Detection of Bars in Galaxies using a Deep Convolutional Neural Network (https://arxiv.org/abs/1711.04573) by Sheelu Abraham, Arun Aniyan, Ajit K. Kembhavi, N. S. Philip, Kaustubh Vaghmare
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Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.50 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.25 |
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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.
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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
See Input Summary
See Input Summary
Resources
Vendor resources
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