%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Vit Huge Patch14 Clip 224.laion2b
ViT-Huge visual encoder based on the CLIP framework, trained on the laion2B dataset, supports image feature extraction
Downloads 1,969
Release Time : 12/24/2024
Model Overview
This is a large visual encoder model based on the Vision Transformer architecture, specifically designed for extracting high-level feature representations from images. As the image encoding component of the CLIP model, it can map images to a semantic space aligned with text.
Model Features
Large-scale pre-training
Pre-trained on the laion2B ultra-large dataset, containing billions of image-text pairs
High-resolution processing
Supports input resolution of 224x224 pixels, suitable for processing images with rich details
Cross-modal alignment
As part of the CLIP model, the learned feature space is aligned with the text semantic space
Efficient Transformer architecture
Adopts the Vision Transformer architecture with powerful global modeling capabilities
Model Capabilities
Image feature extraction
Visual semantic understanding
Cross-modal representation learning
Image classification
Image retrieval
Use Cases
Computer vision
Zero-shot image classification
Implements image classification without specific training using the CLIP framework
Image retrieval
Image search system based on semantic similarity
Multimodal applications
Image-text matching
Determines whether an image and text description match
Visual question answering
Serves as the visual feature extraction module for multimodal systems
Featured Recommended AI Models