# An Extended Evaluation Split for DeepSpaceYoloDataset

arXiv: https://arxiv.org/abs/2604.27593 · HTML: https://mlsanity.com/paper/2604.27593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Astroalign: A Python module for astronomical image registration](https://mlsanity.com/paper/1909.02946/) (2019)
- [Robustness analysis of Deep Sky Objects detection models on HPC](https://mlsanity.com/paper/2508.09831/) (2025)

## Abstract (arXiv)

Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develop highly effective detection solutions that are accessible to a wide audience, rather than being reserved for major scientific observatories. Published in 2023, DeepSpaceYoloDataset is a collection of annotated images created to train YOLO-based models for detecting Deep Sky Objects, particularly suited for Electronically Assisted Astronomy. In this paper, we present an update to DeepSpaceYoloDataset with the addition of a new split, test2026, designed to evaluate detection models with a greater diversity of images.