  Galaxy morphological classification is a fundamental aspect of galaxy
formation and evolution studies. Various machine learning tools have been
developed for automated pipeline analysis of large-scale surveys, enabling a
fast search for objects of interest. However, crowded regions in the image may
pose a challenge as they can lead to bias in the learning algorithm. In this
Research Note, we present galmask, an open-source package for unsupervised
galaxy masking to isolate the central object of interest in the image. galmask
is written in Python and can be installed from PyPI via the pip command.
