vit_huge_patch14_clip_quickgelu_378.dfn5b Open-source Image Encoder - Achieving Efficient Image Coding Processing

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Vit Huge Patch14 Clip Quickgelu 378.dfn5b

Developed by timm

ViT-Huge image encoder based on CLIP architecture, trained on DFN5B dataset, supports quick GELU activation

Image Classification

Transformers

Open Source License:Other #CLIP image encoder #Large-scale visual feature extraction #Zero-shot image classification

Downloads 27

Release Time : 12/26/2024

Model Overview

This model is the visual encoder part of the CLIP framework, using Vision Transformer (ViT) architecture, specially designed for efficient image feature extraction tasks.

Model Features

Large-scale ViT architecture

Uses ViT-Huge architecture with stronger feature extraction capabilities

Quick GELU activation

Uses QuickGELU activation function to improve computational efficiency

CLIP-compatible design

As part of the CLIP framework's visual encoder, it can be used with text encoders

Large-scale pre-training

Trained on DFN5B dataset with powerful visual representation capabilities

Model Capabilities

Image feature extraction

Visual representation learning

Cross-modal alignment

Use Cases

Computer vision

Image classification

Extract image features for classification tasks

Image retrieval

Generate image embeddings for similarity search

Multimodal applications

Image-text matching

Work with text encoders to achieve cross-modal image-text matching

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Vit Huge Patch14 Clip Quickgelu 378.dfn5b

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 vit_huge_patch14_clip_quickgelu_378.dfn5b

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