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Siglip Base Patch16 224

Developed by google
SigLIP is a vision-language model pretrained on the WebLi dataset, utilizing an improved Sigmoid loss function to optimize image-text matching tasks
Downloads 250.28k
Release Time : 9/30/2023

Model Overview

SigLIP is an improved version of the CLIP model, enhancing image-text matching through the Sigmoid loss function, suitable for tasks like zero-shot image classification and image-text retrieval

Model Features

Improved Sigmoid loss function
Eliminates the need for global similarity normalization and performs excellently in both small and large batch scenarios
Efficient pretraining
Pretrained on the large-scale WebLI dataset, learning rich visual-language representations
Zero-shot capability
Can be directly applied to image classification and retrieval tasks without fine-tuning

Model Capabilities

Zero-shot image classification
Image-text retrieval
Multimodal understanding

Use Cases

Image understanding
Animal recognition
Identify animal categories in images
Accurately distinguishes common animals like cats and dogs
Scene understanding
Understand scenes and activities in images
Can recognize activities such as 'playing music' or 'doing sports'
Content retrieval
Image-text matching
Retrieve relevant images based on textual descriptions
Efficiently matches images with descriptive texts
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