# Dense feature extraction

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

SigLIP 2 vision-language model trained on WebLI dataset, suitable for zero-shot image classification tasks

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification tasks.

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification tasks

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification

A SigLIP 2 vision-language model trained on the WebLI dataset, supporting zero-shot image classification tasks

SigLIP 2 vision-language model trained on the WebLI dataset, suitable for zero-shot image classification tasks

SigLIP 2 vision-language model trained on WebLI dataset, supporting zero-shot image classification tasks

SigLIP 2 vision-language model trained on the WebLI dataset, supporting zero-shot image classification tasks

SigLIP 2 is a vision-language model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved multilingual vision-language encoder based on SigLIP, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is an improved model based on SigLIP, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.