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Eva Giant Patch14 Clip 224.laion400m S11b B41k
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Eva Giant Patch14 Clip 224.laion400m S11b B41k

Developed by timm
A 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#Multimodal Pre-training#Large-scale Dataset Training
Downloads 459
Release Time : 4/10/2023

Model Overview

This model is a vision-language model based on the CLIP architecture, capable of mapping images and text into the same semantic space for cross-modal similarity computation and zero-shot image classification.

Model Features

Zero-shot Learning Capability
Can perform new visual tasks without task-specific fine-tuning
Cross-modal Understanding
Capable of understanding both image and text content, establishing semantic connections between them
Large-scale Pre-training
Pre-trained on large-scale datasets such as LAION-400M

Model Capabilities

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

Use Cases

Content Moderation
Inappropriate Content Detection
Automatically identify potentially inappropriate content in images
E-commerce
Product Categorization
Classify product images based on natural language descriptions
Media Analysis
Image Captioning
Generate semantically relevant text descriptions for images
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