S

Siglip2 So400m Patch16 384

Developed by google
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.
Downloads 7,632
Release Time : 2/17/2025

Model Overview

This model can be directly used for tasks such as zero-shot image classification and image-text retrieval, or serve as a visual encoder for vision-language models.

Model Features

Enhanced Semantic Understanding
Integrates multiple technologies to improve semantic understanding, including decoder loss, global-local and masked prediction loss.
Zero-shot Image Classification
Supports classifying images without specific training, suitable for various scenarios.
Dense Feature Extraction
Capable of extracting dense features from images, suitable for vision-language models and other visual tasks.

Model Capabilities

Zero-shot Image Classification
Image-Text Retrieval
Visual Encoding

Use Cases

Image Classification
Zero-shot Image Classification
Classify images without specific training, applicable to various scenarios.
High-accuracy zero-shot classification performance
Visual Encoding
Vision-Language Model
Serves as a visual encoder, providing image features for vision-language models.
Efficient image feature extraction
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase