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Eva Giant Patch14 Clip 224.laion400m
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Eva Giant Patch14 Clip 224.laion400m

Developed by timm
The EVA CLIP model is a vision-language model based on OpenCLIP and the timm framework, supporting zero-shot image classification tasks.
Text-to-Image
Safetensors
Open Source License:MIT#Zero-shot image classification#Large-scale pretraining#Multimodal understanding
Downloads 124
Release Time : 12/26/2024

Model Overview

This model is a vision-language model based on the CLIP architecture, capable of understanding the relationship between images and text, suitable for tasks such as zero-shot image classification.

Model Features

Zero-shot learning capability
The model can classify without specific category training data.
Multimodal understanding
Capable of processing and understanding both image and text information.
Trained on large-scale data
Trained using the LAION-400M dataset, with broad visual concept understanding capabilities.

Model Capabilities

Image classification
Image-text matching
Zero-shot learning

Use Cases

Computer vision
Image classification
Classify image content without specific category training data.
Performs well in various image classification tasks
Image search
Search for relevant images based on text descriptions.
Can accurately match images with text descriptions
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