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Siglip So400m Patch16 256 I18n

Developed by google
A multimodal model based on the SoViT backbone network, improved with the Sigmoid loss function, supporting zero-shot image classification and image-text retrieval
Downloads 230
Release Time : 10/21/2024

Model Overview

SigLIP is an improved vision-language pretraining model based on CLIP, optimized with the Sigmoid loss function for better training efficiency, supporting larger batch training and performing better in small batch scenarios

Model Features

Sigmoid Loss Function
Operates only on image-text pairs, eliminating the need for global similarity normalization, and supports larger batch training
Computationally Optimal Architecture
Uses the shape-optimized SoViT-400m version to maximize computational efficiency
Multilingual Support
Pretrained on 256-resolution multilingual corpora, supporting international applications

Model Capabilities

Zero-shot Image Classification
Image-Text Retrieval
Multimodal Understanding

Use Cases

Content Classification
Animal Recognition
Identify animals such as cats and dogs in images
Examples show accurate differentiation between cat and dog images
Media Analysis
Scene Understanding
Identify activity types in images (e.g., playing music, sports)
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