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Eva Giant Patch14 Plus Clip 224.merged2b S11b B114k
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Eva Giant Patch14 Plus Clip 224.merged2b S11b B114k

Developed by timm
EVA-Giant is a large-scale vision-language model based on the CLIP architecture, supporting zero-shot image classification tasks.
Text-to-Image
Safetensors
Open Source License:MIT#Zero-shot Image Classification#Large-scale Pretraining#Multimodal Understanding
Downloads 1,080
Release Time : 4/10/2023

Model Overview

This model is a vision-language pretrained model based on the CLIP architecture, capable of understanding the relationship 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 a vast number of image-text pairs, learning rich visual concepts
Cross-modal Understanding
Capable of simultaneously understanding image content and related textual descriptions

Model Capabilities

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

Use Cases

Content Management
Automatic Image Tagging
Automatically generates descriptive labels for unlabeled images
Improves image retrieval efficiency
E-commerce
Product Categorization
Automatically classifies product images into relevant categories
Reduces manual labeling costs
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