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Eva02 Enormous Patch14 Clip 224.laion2b
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Eva02 Enormous Patch14 Clip 224.laion2b

Developed by timm
EVA-CLIP is 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#Large-scale pre-training#Multimodal contrastive learning
Downloads 38
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
Supports zero-shot image classification, enabling classification without task-specific training data.
Vision-language alignment
Achieves alignment between visual and language modalities through joint training of image and text encoders.
High performance
Demonstrates excellent performance on multiple benchmark datasets, with high classification accuracy.

Model Capabilities

Zero-shot image classification
Image-text matching
Vision-language understanding

Use Cases

Image classification
Zero-shot image classification
Classify images using natural language descriptions without task-specific training data.
Vision-language tasks
Image-text matching
Determine whether an image and a text description match.
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