| license: apache-2.0 | |
| tags: | |
| - vision | |
| # SigLIP 2 Base | |
| [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 | |
| ``` | |