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docs: update README.md #53

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Expand Up @@ -184,7 +184,7 @@ We replace the vision backbone and keep the same LLM and training recipe as in L

Probing 3D Awareness: we use the code from [Probing the 3D Awareness of Visual Foundation Models](https://github.com/mbanani/probe3d) and
evaluate our RADIO model and its teachers on monocular depth,
surface normals and multi-view correspondance tasks, using the
surface normals and multi-view correspondence tasks, using the
NAVI dataset. For each task we report the accuracy, averaged
over all thresholds. RADIO preserves features of DINOv2 and
performs much better than CLIP analogs.
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