Vit Medium Patch16 Clip 224.tinyclip Yfcc15m
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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
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