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Siglip2 Base Patch16 256

Developed by google
SigLIP 2 is a multilingual vision-language encoder with improved semantic understanding, localization, and dense feature extraction capabilities.
Downloads 45.24k
Release Time : 2/17/2025

Model Overview

Building upon SigLIP, SigLIP 2 integrates multiple technologies to enhance performance on vision-language tasks, applicable to zero-shot image classification and image-text retrieval.

Model Features

Enhanced Semantic Understanding
Improved semantic comprehension through techniques like decoder loss integration.
Enhanced Localization Capability
Utilizes global-local and masked prediction losses to improve localization accuracy.
Dense Feature Extraction
Optimized dense feature extraction suitable for various vision tasks.
Aspect Ratio and Resolution Adaptability
Supports multiple aspect ratios and resolutions, enhancing model flexibility.

Model Capabilities

Zero-shot Image Classification
Image-Text Retrieval
Visual Feature Extraction

Use Cases

Image Classification
Zero-shot Image Classification
Classifies images without fine-tuning, supporting custom labels.
Demonstrates excellent performance across multiple datasets.
Image-Text Retrieval
Cross-modal Retrieval
Retrieves relevant images based on text or relevant text based on images.
Pre-trained on WebLI dataset with strong retrieval capabilities.
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