GitHub / shilpakancharla / galaxy-image-classification
SDSS telescopes have captured over 40 TB worth of galaxy images and classification of these images is the first step towards obtaining a deeper understanding of physical processes within them, star formation, and the nature of the universe. Since we could not find an easily accessible dataset for galaxy classification, we compiled a dataset for Galaxy classification and provided benchmarks with some of the common learning algorithms that would help in automating the galaxy classification which until recently had to be performed by hand by expert astronomers. We classify the images of galaxies into four classes: spiral, elliptical, irregular, and invalid.
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PURL: pkg:github/shilpakancharla/galaxy-image-classification
Stars: 4
Forks: 3
Open issues: 0
License: None
Language: Python
Size: 3.83 MB
Dependencies parsed at: Pending
Created at: over 4 years ago
Updated at: 3 months ago
Pushed at: over 4 years ago
Last synced at: 2 months ago
Topics: astronomical-images, astronomy, astropy, big-data, convolutional-neural-networks, deep-learning, galaxy-classification, image-classification, machine-learning, multilayer-perceptron-network, principal-component-analysis, random-forest, support-vector-machines