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Vit Huge Patch14 Clip 378.dfn5b
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Vit Huge Patch14 Clip 378.dfn5b

Developed by timm
The visual encoder component of DFN5B-CLIP, based on ViT-Huge architecture, trained with 378x378 resolution images for CLIP model
Image Classification
Transformers
Open Source License:Other#Large-scale visual feature extraction#Zero-shot image classification#High-resolution image processing
Downloads 461
Release Time : 12/26/2024

Model Overview

This model serves as the visual encoder component of CLIP (Contrastive Language-Image Pretraining), specifically designed for extracting high-level feature representations from images. Built on the Vision Transformer (ViT) architecture, it is suitable for various computer vision tasks.

Model Features

High-resolution processing
Supports high-resolution image inputs of 378x378 pixels, capable of capturing finer visual features
CLIP compatibility
As the visual encoder component of the CLIP model, it can work with text encoders to achieve cross-modal understanding
ViT-Huge architecture
Based on the large-scale Vision Transformer architecture, it possesses powerful feature extraction capabilities

Model Capabilities

Image feature extraction
Visual representation learning
Cross-modal alignment

Use Cases

Computer vision
Image classification
Utilizes extracted image features for classification tasks
Image retrieval
Image search based on visual similarity
Multimodal applications
Image-text matching
Combines with text encoders to achieve image-text relevance judgment
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