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Vit Medium Patch32 Clip 224.tinyclip Laion400m
V
Vit Medium Patch32 Clip 224.tinyclip Laion400m
Developed by timm
A vision-language model based on the OpenCLIP library, supporting zero-shot image classification tasks.
Downloads 110
Release Time : 3/20/2024
Model Overview
This model is a vision-language model based on the Vision Transformer (ViT) architecture, primarily designed for zero-shot image classification tasks. It combines the representational capabilities of images and text, enabling image classification without task-specific training.
Model Features
Zero-shot learning
Capable of classifying images without task-specific training, suitable for various scenarios.
Joint vision-language representation
Combines the representational capabilities of images and text to enhance model generalization.
Based on ViT architecture
Utilizes the Vision Transformer architecture for efficient image data processing.
Model Capabilities
Zero-shot image classification
Image representation learning
Text representation learning
Use Cases
Image classification
Zero-shot image classification
Classify images without task-specific training.
Multimodal applications
Image retrieval
Retrieve relevant images based on text queries.
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