Deep Learning

Motivation

In my home environment where I use NFS to store JupyterLab notebooks, I measured the performance when the NFS server is a RaspberryPi or HP Z240, and found that in the learning loop (state in which epochs are stacked), there is no significant difference whether the NB is stored on an NFS server or locally. I found that there was no significant difference between NBs stored on an NFS server or locally.

Therefore, I have challenged to speed up the learning process, and I summarize the progress/results here.

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.

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.

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).

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.

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.

Continued from previous article.

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