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}, journal = {under review} }
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
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