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Vit Betwixt Patch32 Clip 224.tinyclip Laion400m
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Vit Betwixt Patch32 Clip 224.tinyclip Laion400m

Developed by timm
A small CLIP model based on ViT architecture, suitable for zero-shot image classification tasks, trained on the LAION-400M dataset.
Image Classification
Safetensors
Open Source License:MIT#Zero-shot image classification#CLIP architecture#Small-scale pretraining
Downloads 113
Release Time : 3/20/2024

Model Overview

This model combines Vision Transformer (ViT) and CLIP architectures, enabling zero-shot image classification, meaning it can classify images without specific training.

Model Features

Zero-shot learning capability
Can perform image classification tasks without fine-tuning for specific tasks
Small-scale and efficient
Compared to large CLIP models, this model has fewer parameters and is suitable for resource-limited environments
Multimodal understanding
Capable of understanding both image and text information, establishing correlations between them

Model Capabilities

Zero-shot image classification
Image-text matching
Multimodal feature extraction

Use Cases

Content classification
Automatic tagging of social media images
Automatically generates relevant tags for images uploaded to social media
Improves content classification efficiency and reduces manual labeling requirements
E-commerce
Product image search
Searches for relevant product images through text descriptions
Enhances user experience and search efficiency
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