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Vit Base Patch16 Clip 224.dfn2b
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Vit Base Patch16 Clip 224.dfn2b

Developed by timm
Vision Transformer model based on CLIP architecture, featuring DFN2B-CLIP image encoder weights released by Apple
Image Classification
Transformers
Open Source License:Other#CLIP visual encoding#Zero-shot image classification#Multimodal pre-training
Downloads 444
Release Time : 12/26/2024

Model Overview

This model is a Vision Transformer (ViT) based on the CLIP architecture, specifically designed for image feature extraction. It employs a patch16 input processing method with an input resolution of 224x224 pixels.

Model Features

CLIP Architecture
Utilizes Contrastive Language-Image Pre-training (CLIP) architecture with powerful image representation capabilities
ViT-B/16 Foundation
Based on the Vision Transformer base architecture with 16x16 patch size
Efficient Feature Extraction
Optimized for image feature extraction, suitable as a backbone network for vision tasks

Model Capabilities

Image feature extraction
Visual representation learning

Use Cases

Computer Vision
Image Classification
Can serve as a feature extractor for image classification tasks
Image Retrieval
Used to extract image features for similar image retrieval
Multimodal Learning
Vision-Language Tasks
Can serve as the visual encoder component for vision-language models
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