Hugging Face· Platform

SigLIP 2: A better multilingual vision language encoder

Today Google releases a new and better family of multilingual vision-language encoders, SigLIP 2 . The authors have extended the training objective of SigLIP ( sigmoid loss ) with additional objectives for improved semantic understanding, localization, and dense features. Additional objectives (Source: https://huggingface.co/papers/2502.14786 ) SigLIP 2 models outperform the older SigLIP ones at all model scales in core capabilities, including zero-shot classification, image-text retrieval, and transfer performance when extracting visual representations for Vision-Language Models (VLMs). A cherry on top is the dynamic resolution ( naflex ) variant. This is useful for downstream tasks sensitive to aspect ratio and resolution. Here is a list of all the models released: Size Patch Size Resolution Transformers JAX Base (86M) 32 256 google/siglip2-base-patch32-256 google/siglip2-base-patch32-256-jax 16 224 …

Announced

21 Feb 2025 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+528d after)