# High Semantic Understanding

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

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

Vision Transformer model based on SigLIP 2 architecture, pre-trained on WebLI dataset, with attention pooling head removed and global average pooling applied

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

A vision Transformer model based on the SigLIP 2 architecture, featuring a Global Average Pooling (GAP) variant that removes the attention pooling head, suitable for image feature extraction tasks.

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

A ViT image encoder based on SigLIP 2, utilizing global average pooling and removing the attention pooling head, suitable for image feature extraction tasks.

SigLIP 2 ViT image encoder, using global average pooling, with attention pooling head removed, designed specifically for timm

Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction

ViT image encoder based on SigLIP 2, using Global Average Pooling (GAP) instead of attention pooling head, suitable for image feature extraction tasks.

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

A ViT image encoder based on SigLIP 2 for extracting image features, supporting multilingual vision-language tasks.

A ViT image encoder based on SigLIP 2, utilizing global average pooling, suitable for vision-language tasks.