V

Vit Medium Patch16 Clip 224.tinyclip Yfcc15m

Developed by timm
CLIP model based on ViT architecture for zero-shot image classification tasks
Downloads 144
Release Time : 3/20/2024

Model Overview

This model is part of the OpenCLIP project, utilizing the Vision Transformer (ViT) architecture, specifically designed for zero-shot image classification tasks. It combines visual and language representations, enabling image classification without task-specific training.

Model Features

Zero-shot learning capability
Performs image classification tasks without task-specific training data
Multimodal understanding
Processes both visual and textual information for cross-modal understanding
Efficient architecture
Based on ViT architecture, balancing model performance and computational efficiency

Model Capabilities

Zero-shot image classification
Image-text matching
Cross-modal retrieval

Use Cases

Content management
Automatic image tagging
Automatically generates descriptive tags for images in a library
Improves image retrieval efficiency and reduces manual labeling costs
E-commerce
Product categorization
Automatically classifies product images into relevant categories
Enhances product listing efficiency and improves user experience
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase