Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches

The "Machine learning for morphological galaxy classification" is a repository for classifying Galaxy Zoo 2 (GZ2) images into (1) Galaxy and Non-Galaxy, and (2) Galaxy in Spiral, Elliptical, and Odd objects using the five state-of-the-art machine learning models. Overview We employed five different classification models, including: Support Vector Machine (SVM) with Zernike moments (ZMs) 1D-Convolutional Neural Network (1D-CNN) with ZMs 2D-CNN with Vision Transformer (ViT) and original images ResNet50 with ViT and original images VGG16 with ViT and original images The SVM and 1D-CNN models utilized Zernike moments (ZMs) extracted from the images, while the 2D-CNN, ResNet5, and VGG16 with Vision Transformer (ViT) models were designed based on the original images. You can see the full notebooks and data in: https://github.com/hmddev1/machine_learning_for_morphological_galaxy_classification/tree/main and the full changelog in: https://github.com/hmddev1/machine_learning_for_morphological_galaxy_classification/commits/v0.1