---
license: apache-2.0
language:
- en
---

# STRADAViT
Self-supervised Vision Transformers for Radio Astronomy Discovery Algorithms

## License
This model is released under the Apache License 2.0.

## Citation
If you use STRADAViT in research, please cite the associated work:

@article{demarco2026stradavit,
  title         = {STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer},
  author        = {DeMarco, Andrea and Fenech Conti, Ian and Camilleri, Hayley and Bushi, Ardiana and Riggi, Simone},
  year          = {2026},
  note          = {Under review},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.IM},
  url           = {https://arxiv.org/abs/2603.29660v3}
}

## Acknowledgement
This model was developed as part of the STRADA project on self-supervised transformers for radio astronomy.
If you build on this model, please acknowledge the project and cite the associated publication.

## Intended Use
STRADAViT is intended as a domain-adapted starting point for radio astronomy imaging tasks.
It is suitable for:

- frozen-backbone transfer via linear probing
- downstream fine-tuning for morphology classification
- reuse as a vision backbone in broader radio astronomy pipelines, including detection and segmentation models

## Limitations
STRADAViT is trained for transfer on radio astronomy imaging and should not be assumed to 
outperform all off-the-shelf vision backbones in every downstream setting. In the current study:

- gains are strongest under frozen-backbone evaluation
- fine-tuning gains are more dataset-dependent
- performance remains sensitive to view generation and dataset heterogeneity
- broader validation on additional surveys and downstream tasks is still needed

## Class Files

HF-style classes for using STRADAViT can be found on [GitHub](https://github.com/andreademarco86/stradavit).