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Vit Base Patch16 Clip 224.metaclip 400m
V
Vit Base Patch16 Clip 224.metaclip 400m
Developed by timm
A dual-framework compatible vision model trained on the MetaCLIP-400M dataset, supporting both OpenCLIP and timm frameworks
Downloads 1,206
Release Time : 10/23/2024
Model Overview
This is a vision model based on the Vision Transformer architecture, specifically designed for zero-shot image classification tasks. The model is trained on the MetaCLIP-400M dataset and is compatible with both OpenCLIP and timm frameworks.
Model Features
Dual Framework Compatibility
Supports both OpenCLIP and timm frameworks, offering more flexible usage options
Zero-shot Learning Capability
Capable of classifying new categories without specific training
QuickGELU Activation
Uses the quickgelu variant, potentially providing faster training and inference speeds
Model Capabilities
Zero-shot Image Classification
Image Feature Extraction
Cross-modal Representation Learning
Use Cases
Computer Vision
Open-domain Image Classification
Classify images of arbitrary categories without specific training
Image Retrieval
Semantic similarity-based image search
Multimodal Applications
Image-Text Matching
Evaluate the matching degree between images and text descriptions
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