AIbase
EN
Home
Vit Base Patch16 Siglip 256.v2 Webli
V

Vit Base Patch16 Siglip 256.v2 Webli

Developed by timm
A ViT image encoder based on SigLIP 2 for extracting image features, supporting multilingual vision-language tasks.
Text-to-Image
Transformers
Open Source License:Apache-2.0#Multimodal Visual Encoding#High Semantic Understanding#Dense Feature Extraction
Downloads 731
Release Time : 2/21/2025

Model Overview

This is a Vision Transformer model based on SigLIP 2, specifically designed for image feature extraction. It serves as the visual encoder part described in the SigLIP 2 paper and is suitable for various vision-language tasks.

Model Features

Enhanced Semantic Understanding
Based on the SigLIP 2 architecture, it has improved semantic understanding capabilities
Localization Capability
Improved ability to localize objects in images
Dense Feature Extraction
Capable of extracting richer dense image features
Sigmoid Loss Function
Uses Sigmoid loss for language-image pretraining, enhancing model performance

Model Capabilities

Image Feature Extraction
Vision-Language Understanding
Multimodal Representation Learning

Use Cases

Computer Vision
Image Retrieval
Efficient image retrieval using extracted image features
Visual Question Answering
Serves as a visual encoder for visual question answering systems
Multimodal Applications
Image-Text Matching
Used for image and text matching tasks
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase