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CLIP ViT B 32 Laion2b E16
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CLIP ViT B 32 Laion2b E16

Developed by justram
A vision-language pretrained model implemented based on OpenCLIP, 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 89
Release Time : 5/17/2023

Model Overview

This model is an implementation of the CLIP architecture, combining Vision Transformer (ViT) and text encoder, capable of understanding the correlation between images and texts, 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
Cross-modal understanding
Capable of processing and understanding both visual and textual information
Large-scale pretraining
Pretrained on the laion2B dataset, with strong generalization capabilities

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
Automatically classify product images based on descriptions
Media analysis
Image captioning
Generate descriptive labels for images
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