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Vit Giant Patch14 Clip 224.laion2b
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Vit Giant Patch14 Clip 224.laion2b

Developed by timm
Vision Transformer model based on CLIP architecture, designed for image feature extraction, trained on the laion2B dataset
Image Classification
Transformers
Open Source License:Apache-2.0#Multimodal Image Encoding#Zero-shot Image Classification#Large-scale Pretraining
Downloads 71
Release Time : 12/24/2024

Model Overview

This is a Vision Transformer model based on the CLIP architecture, primarily used for image feature extraction tasks. The model adopts the ViT-Giant architecture with a patch size of 14 and an input resolution of 224x224, trained on the laion2B dataset.

Model Features

Large-scale Pretraining
Pretrained on the laion2B large-scale dataset, with powerful visual representation capabilities
CLIP Architecture
Adopts a contrastive learning framework to learn joint representation spaces for images and text
ViT-Giant Architecture
Uses a giant variant of Vision Transformer with enhanced feature extraction capabilities

Model Capabilities

Image Feature Extraction
Visual Representation Learning
Cross-modal Retrieval

Use Cases

Computer Vision
Image Retrieval
Content-based image retrieval system
High-precision retrieval of similar images
Zero-shot Classification
Classify new categories without specific training
Multimodal Applications
Image-Text Matching
Determine if an image and text description match
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