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Style 250412.vit Base Patch16 Siglip 384.v2 Webli
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Style 250412.vit Base Patch16 Siglip 384.v2 Webli

Developed by p1atdev
A vision model based on the Vision Transformer architecture, trained using SigLIP (Sigmoid Loss for Language-Image Pretraining), suitable for image understanding tasks.
Image Classification
Transformers
#High-resolution image processing#Zero-shot learning#Multimodal pre-training
Downloads 66
Release Time : 4/12/2025

Model Overview

This model is a vision model based on the Vision Transformer architecture, pre-trained on large-scale web image data using the SigLIP method, excelling in visual understanding tasks such as image classification and retrieval.

Model Features

SigLIP Pre-training
Uses Sigmoid loss function for language-image contrastive learning, more efficient compared to traditional Softmax methods
Large-scale Data Training
Pre-trained on the WebLI v2 dataset, containing billions of web images
High-resolution Processing
Supports 384x384 pixel input, suitable for tasks requiring fine visual features

Model Capabilities

Image feature extraction
Zero-shot image classification
Cross-modal retrieval

Use Cases

Content Retrieval
Text-based Image Search
Retrieve relevant images using text queries
Performs excellently on WebLI benchmark tests
Image Classification
Zero-shot Classification
Classify new categories without fine-tuning
Performs well on benchmarks like ImageNet
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