Update README.md

README.md

@@ -1,3 +1,41 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# 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