Galaxy on akenji's lab

About the Galaxy Morphological Classification Dataset

Thu, 23 Feb 2023 00:00:00 +0000

In the beginning

In the five articles from this article to this article Galaxy shape(morphological) classification was performed using CNN (VGG16, ResNet) and ViT. In this article, I would like to consider the dataset used for the galaxy morphological classification, as I would like to re-examine the dataset when analyzing errors in the future.

Galaxy Shape Classification by Deep Learning (ViT)（Part 2）

Sun, 19 Feb 2023 00:00:00 +0000

Introduction.

In this article, we performed galaxy shape classification using the Vision Transformer (ViT) of Transformer’s adaptation to image processing. This time, we performed the same classification using a model already trained as a model for ViT.

Galaxy Shape Classification by Deep Learning (ViT)（Part 1）

Mon, 09 Jan 2023 00:00:00 +0000

Introduction

In this article at the end of last year, we mentioned that we performed galaxy shape classification with CNN (VGG16, ResNet). In this article, we will perform galaxy shape classification with a Transformer-based Vison Transformer (ViT).

Galaxy Shape Classification by Deep Learning (CNN) (Part 1)

Fri, 30 Dec 2022 00:00:00 +0000

Introduction

I have been studying Deep Learning for a while and thought I would try it out in my field of interest. I like astronomy and am particularly interested in stellar evolution, the formation of elements, and galaxy formation and evolution. I tried to classify the shape of galaxies, which seemed to be relatively easy to do.

Galaxy Shape Classification by Deep Learning (CNN)（Part 2）

Fri, 30 Dec 2022 00:00:00 +0000

Continued from previous article.

Why doesn’t the training/testing error improve after repeated epochs? The results of various attempts to answer this question are summarized below.

Galaxy Shape Classification by Deep Learning (CNN)（Part 3）

Fri, 30 Dec 2022 00:00:00 +0000

Continued from previous article.

In the previous, we studied with VGG16, but this time we will use ResNet for the model.