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        "description": "# Galaxy2Galaxy [![Build Status](https://travis-ci.org/ml4astro/galaxy2galaxy.svg?branch=master)](https://travis-ci.org/ml4astro/galaxy2galaxy) [![Documentation Status](https://readthedocs.org/projects/galaxy2galaxy/badge/?version=latest)](https://galaxy2galaxy.readthedocs.io/en/latest/?badge=latest) [![Join the chat at https://gitter.im/ml4astro/galaxy2galaxy](https://badges.gitter.im/ml4astro/galaxy2galaxy.svg)](https://gitter.im/ml4astro/galaxy2galaxy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) \n\nGalaxy2Galaxy, 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\nlearning models applied to astronomical image processing problems.\n\n## Install\n\nG2G can easily be installed using pip:\n```\n$ pip install galaxy2galaxy[galsim]\n```\n\nShould the install fail because of the GalSim dependency, check the GalSim [install\nguide]( https://github.com/GalSim-developers/GalSim/blob/releases/2.1/INSTALL.md) to fix it.\n\n## Usage\n\nTo generate the COSMOS 25.2 sample at native pixel scale and stamp size:\n\n```bash\n$ g2g-datagen --problem=img2img_cosmos --data_dir=data/img2img_cosmos\n```\nThis uses GalSim to draw postage stamps and save them in TFRecord format which can then be used for training.\n\nTo  train an autoencoder with this data:\n```bash\n$ 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\n```",
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