An Extended Evaluation Split for DeepSpaceYoloDataset
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914520266440704 |
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| author | Parisot, Olivier |
| author_facet | Parisot, Olivier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27593 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Extended Evaluation Split for DeepSpaceYoloDataset Parisot, Olivier Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition 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. |
| title | An Extended Evaluation Split for DeepSpaceYoloDataset |
| topic | Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.27593 |