Welcome to galaxyHackers documentation!
-----------------------------------------

.. image:: _static/logo.png
   :alt: Galaxy Hackers Logo
   :width: 200px
   :align: center

GalaxyHackers is an innovative toolkit for astronomical data analysis, with a focus on galaxy cluster detection.
The project provides utilities for building, training, and evaluating machine learning models specifically tailored to the unique challenges of astrophysical datasets.

Key Features
************

-  Fetches and processes astronomical data.
-  Annotates and visualizes galaxy images dynamically.
-  Trains and evaluates YOLO models for galaxy and cluster detection.

Features
********
-  Supports a variety of deep learning models: ResNet18, EfficientNet, DenseNet, ViTL16, etc.
-  Integrates with Comet ML for tracking training experiments.
-  Includes automatic segmentation plot generation after model training.
-  Provides flexibility in choosing optimizers, learning rate schedulers, and hyperparameters.

.. toctree::
   :maxdepth: 2
   :caption: galaxyHackers Overview

   topics/overview
   topics/license

.. toctree::
   :maxdepth: 2
   :caption: Get Started

   topics/installation
   topics/usage
   guides/development

.. toctree::
   :maxdepth: 2
   :caption: Extra

   topics/changelog
   modules/index

GitHub Repository: https://github.com/pelancha/galaxyHackers