# CONVOLUTED COSMOS : Automatic Classification of Galaxy Images Using Deep Learning
In this project, Galaxy Image Classification using a Deep Convolution Neural Network is presented. The galaxy can be classified based on its features into three main categories, namely: Elliptical, Spiral, and Irregular. The proposed deep galaxy architecture consists of one input convolutional layer having 16 filters, followed by 3 hidden layers, 1 penultimate dense layer and an Output Softmax layer. It is trained over 3232 images for 200 epochs and achieved a testing accuracy 97.38% which outperformed conventional classifiers like Support Vector Machine and previous research contributions in the same domain of Galaxy Image Classification.

Dataset: The galaxy photos were collected from: http://hubblesite.org/images/gallery and https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/data and the dataset was then handcrafted and augmented as per requirement.