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Eva02 Enormous Patch14 Clip 224.laion2b S4b B115k
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Eva02 Enormous Patch14 Clip 224.laion2b S4b B115k

Developed by timm
Large-scale vision-language model based on EVA02 architecture, supporting zero-shot image classification tasks
Text-to-Image
Safetensors
Open Source License:MIT#Zero-shot Image Classification#Multimodal Contrastive Learning#Large-scale Pretraining
Downloads 130
Release Time : 4/10/2023

Model Overview

This model is a vision-language pretrained model based on the CLIP framework, utilizing the EVA02 architecture. It can understand the correlation between images and text, suitable for cross-modal tasks such as zero-shot image classification.

Model Features

Zero-shot Learning Capability
Can perform image classification tasks without task-specific fine-tuning
Large-scale Pretraining
Pretrained on large-scale datasets such as LAION-2B
Cross-modal Understanding
Capable of processing and understanding both visual and textual information

Model Capabilities

Zero-shot Image Classification
Image-Text Matching
Cross-modal Retrieval

Use Cases

Content Understanding and Retrieval
Intelligent Image Search
Search for relevant images using natural language descriptions
High-precision cross-modal retrieval results
Automatic Image Tagging
Generate descriptive labels for images
Generates relevant labels without training
Education and Research
Visual Concept Learning
Study the associative representation of visual and language concepts
Provides tools for cognitive science research
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