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README.md

@@ -2,15 +2,14 @@
 license: apache-2.0
 tags:
 - vision
+pipeline_tag: zero-shot-image-classification
 ---
 
 # SigLIP 2 Large
 
-[SigLIP 2](https://huggingface.co/collections/google/siglip2-67b5dcef38c175486e240107)
-extends the pretraining objective of
-[SigLIP](https://huggingface.co/collections/google/siglip-659d5e62f0ae1a57ae0e83ba)
-with prior, independently developed techniques into a unified recipe, for improved semantic
-understanding, localization, and dense features.
+[SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
+[SigLIP](https://huggingface.co/papers/2303.15343) with prior, independently developed techniques
+into a unified recipe, for improved semantic understanding, localization, and dense features.
 
 ## Intended uses
 
@@ -38,10 +37,18 @@ The model was trained on up to 2048 TPU-v5e chips.
 
 Evaluation of SigLIP 2 is shown below (taken from the paper).
 
-[Evaluation Table](TODO)
+![Evaluation Table](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)
 
 ### BibTeX entry and citation info
 
 ```bibtex
-TODO
+@misc{tschannen2025siglip2multilingualvisionlanguage,
+      title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, 
+      author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
+      year={2025},
+      eprint={2502.14786},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.14786}, 
+}
 ```