# Dense Feature Extraction

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

ViT image encoder based on SigLIP 2, using global average pooling, with 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

SigLIP 2 ViT model, containing only the image encoder part for image feature extraction, 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

A ViT image encoder based on SigLIP 2, employing 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, 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.

A vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction, using Global Average Pooling (GAP) instead of attention pooling head

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.

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 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

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

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

A vision Transformer model based on SigLIP 2, using Global Average Pooling (GAP) instead of attention pooling head for image encoding

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

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

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

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

Vision Transformer model based on SigLIP 2, utilizing global average pooling for image features

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, 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.

ViT model based on SigLIP 2, focused on image feature extraction, trained on the webli dataset

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

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

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

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

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 a multilingual vision-language encoder that integrates SigLIP's pretraining objectives and introduces new training schemes, enhancing semantic understanding, localization, and dense feature extraction capabilities.

SigLIP 2 is a vision-language model based on SigLIP, enhanced with improved semantic understanding, localization, and dense feature extraction capabilities.

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

SigLIP 2 is an advanced vision-language model that integrates 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 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.