# Enhanced Semantic Understanding

ViT image encoder based on SigLIP 2, using global average pooling, with attention pooling head removed, suitable for image feature extraction tasks.

A vision Transformer model based on SigLIP 2, designed for image feature extraction and suitable for multilingual vision-language tasks.

SigLIP 2 ViT model, containing only the image encoder part for image feature extraction, trained on the WebLI dataset.

A Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction and pretrained on the webli dataset.

ViT image encoder based on SigLIP 2, designed for timm, suitable for vision-language tasks

A vision Transformer model based on the SigLIP 2 architecture, designed for image feature extraction, pretrained on the webli dataset

Vision Transformer model based on SigLIP 2 technology, focused on image feature extraction

Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset

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

SigLIP 2 is a vision-language model that integrates multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a multilingual vision-language encoder with improved semantic understanding, localization, and dense feature extraction capabilities.

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

ERNIE 2.0 is a continuous pre-training framework proposed by Baidu in 2019, which gradually constructs and optimizes pre-training tasks through continuous multi-task learning. It outperforms BERT and XLNet in multiple tasks.