Galaxy Image Classification

This repo contains the code for our paper Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing.

Requirements

• numpy==1.19.5
• matplotlib==3.2.1
• tqdm==4.56.0
• torch>=1.7.1
• torchvision>=0.8.2

Select cleandata

• Selecting clean data from the original data downloaded from https://www.kaggle.com/competitions/galaxy-zoo-the-galaxy-challenge/data.

select cleandata.py

Noscheme

• Training the models without HIWL

train_dieleman.py
train_vgg.py
train_googlenet.py
train_resnet26.py
train_resnet.py
train_efficientnet.py
train_vit.py

Scheme

• Training the models with HIWL

train_dieleman.py
train_vgg.py
train_googlenet.py
train_resnet26.py
train_resnet.py
train_efficientnet.py
train_vit.py

Usage

• Training a new model:

1. Selecting cleandata
2. training new model from scheme or noscheme

python select cleandata.py
python train_efficientnet.py

Citation

• If you found this code useful please cite our paper: