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Clip Vit Large Patch14
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Clip Vit Large Patch14

Developed by Xenova
OpenAI's open-source CLIP model, based on Vision Transformer (ViT) architecture, supporting joint understanding of images and text.
Text-to-Image
Transformers
#Multimodal Understanding#Zero-shot Classification#Web Deployment
Downloads 17.41k
Release Time : 9/1/2023

Model Overview

CLIP (Contrastive Language-Image Pretraining) is a multimodal model capable of understanding the relationship between images and text. Trained via contrastive learning, it can be used for tasks such as image classification, image search, and text-to-image retrieval.

Model Features

Multimodal Understanding
Capable of processing and understanding both image and text information, establishing correlations between them.
Zero-shot Learning
Can perform new visual tasks without task-specific fine-tuning.
Web Compatibility
Optimized in ONNX format, supporting execution in browser environments.

Model Capabilities

Image Classification
Image-Text Matching
Text-to-Image Retrieval
Zero-shot Image Recognition

Use Cases

Content Retrieval
Image Search
Search for relevant images based on text descriptions.
Text Search
Search for relevant text descriptions based on image content.
Content Moderation
Inappropriate Content Detection
Detect whether images and text contain inappropriate content.
Creative Assistance
Image Captioning
Automatically generate text descriptions for images.
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