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README.md

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+---
+license: apache-2.0
+tags:
+- vision
+---
+
+# 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.
+
+## Intended uses
+
+You can use the raw model for tasks like zero-shot image classification and
+image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).
+
+
+## Training procedure
+
+SigLIP 2 adds some clever training objectives on top of SigLIP:
+
+1. Decoder loss
+2. Global-local and masked prediction loss
+3. Aspect ratio and resolution adaptibility 
+
+### Training data
+
+SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Compute
+
+The model was trained on up to 2048 TPU-v5e chips.
+
+## Evaluation results
+
+Evaluation of SigLIP 2 is shown below (taken from the paper).
+
+[Evaluation Table](TODO)
+
+### BibTeX entry and citation info
+
+```bibtex
+TODO
+```