Skip to main content


Project description

Galaxy2Galaxy Build Status Documentation Status Join the chat at

Galaxy2Galaxy, or G2G for short, is a library of models, datasets, and utilities to build generative models for astronomical images, based on the Tensor2Tensor library. Similarly to T2T, the goal of this project is to accelerate research in machine learning models applied to astronomical image processing problems.


We recommend users create a conda environment before installing galaxy2galaxy. This makes installing tensorflow and galsim very easy:

$ conda install tensorflow-gpu==1.15
$ conda install -c conda-forge galsim

G2G can then easily be installed using pip inside the environment:

$ pip install git+
$ pip install galaxy2galaxy


To generate the COSMOS 25.2 sample at native pixel scale and stamp size:

$ g2g-datagen --problem=img2img_cosmos --data_dir=data/img2img_cosmos

This uses GalSim to draw postage stamps and save them in TFRecord format which can then be used for training. This assumes that you have downloaded the GalSim COSMOS sample, if that's not the case, you can dowload it with: galsim_download_cosmos -s 25.2

To train an autoencoder with this data:

$ g2g-trainer --data_dir=data/img2img_cosmos --output_dir=training/cosmos_ae   --problem=img2img_cosmos --model=continuous_autoencoder_basic  --train_steps=2000  --eval_steps=100 --hparams_set=continuous_autoencoder_basic

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for galaxy2galaxy, version 0.0.1rc5
Filename, size File type Python version Upload date Hashes
Filename, size galaxy2galaxy-0.0.1rc5.tar.gz (1.6 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page