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Openvision Vit Large Patch14 84
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Openvision Vit Large Patch14 84

Developed by UCSC-VLAA
OpenVision is a fully open, cost-effective family of advanced visual encoders focused on multimodal learning tasks.
Image Classification
Transformers
Open Source License:Apache-2.0#Multimodal visual encoding#Cost-effective model#Open-source Vision Transformer
Downloads 21
Release Time : 5/6/2025

Model Overview

The OpenVision ViT model is a visual encoder based on the Vision Transformer architecture, designed to provide efficient and open visual feature extraction solutions for multimodal learning.

Model Features

Fully open architecture
The model is completely open, allowing researchers and developers to freely use and modify it.
Cost-effective
Optimizes computational resource usage while maintaining high performance, reducing deployment costs.
Multimodal support
Designed for multimodal learning tasks, seamlessly integrable with other modality models.

Model Capabilities

Image feature extraction
Multimodal learning
Visual content understanding

Use Cases

Computer vision
Image classification
Using OpenVision to extract image features for downstream classification tasks.
Visual question answering
Used as a visual encoder in multimodal question-answering systems.
Multimodal applications
Image-text matching
Used for visual feature extraction in image-text retrieval systems.
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