Computer Vision |

How to finetune DINOv2 for astronmy?

Sat, 30 Aug 2025 00:00:00 +0000

When Foundation Models Meet Astronomical Data

Tue, 14 Jan 2025 00:00:00 +0000

Poster presented at the Northern Lights Deep Learning Conference 2025 in Tromsø, Norway. This work evaluates the performance of existing visual foundation models (ViT, DINOv2, CLIP) for astronomical data analysis, with a focus on galaxy morphological classification.

Galaxy Morphological Classification with Deep Learning

Fri, 15 Nov 2024 00:00:00 +0000

Galaxy morphological classification — spiral, elliptical, irregular? — is one of the most natural applications of computer vision in astronomy. Datasets like and provide crowd-sourced morphological labels for thousands of galaxies and serve as standard benchmarks.

In my work with Sébastien Lefèvre, we compared several pre-trained foundation models (ViT, SwinV2, BEiT, DINOv2) on Galaxy10 DECaLS to see how well they transfer to astronomical images. The short answer: surprisingly well, but the fine-tuning strategy matters a lot. These results were presented at and , with a detailed analysis in the .

When Foundation Models Meet Astronomical Data

Mon, 01 Jul 2024 00:00:00 +0000

ePoster presented at the European Astronomical Society Conference 2024 in Padova, Italy, in Session SS10: The impact of the rapidly evolving field of artificial intelligence on astrophysics research: avenues and potential breakthroughs.

This work evaluates the performance of existing visual foundation models (ViT, CLIP, DINOv2) for astronomical data analysis, with a focus on galaxy morphological classification using GalaxyZoo datasets.