README.md

---
license: apache-2.0
tags:
- vision
pipeline_tag: zero-shot-image-classification
---

# SigLIP 2 Large

[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

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](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)

### BibTeX entry and citation info

```bibtex
@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}, 
}
```