# Multimodal Learning

OpenVision is a fully open, cost-effective family of advanced vision encoders for multimodal learning.

OpenVision is a fully open-source, cost-effective family of advanced vision encoders for multimodal learning.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

A PyTorch-based action recognition model for robotics applications

A robot control model based on diffusion policy, released via PyTorchModelHubMixin integration

A zero-shot image classification model based on the open_clip library, specializing in medical field image analysis

GenMedClip is a zero-shot image classification model based on the open_clip library, specializing in medical image analysis.

MoE-LLaVA is a large vision-language model based on the Mixture of Experts architecture, achieving efficient multimodal learning through sparse activation parameters

A zero-shot image classification model based on the Open CLIP library, supporting various vision tasks

A multimodal model based on Microsoft's GIT framework, focused on extracting text from student homework images and generating teacher feedback

A visual question answering model fine-tuned on the textvqa dataset based on microsoft/git-base-textvqa, excelling at handling image-based question answering tasks involving text

A model fine-tuned based on naver-clova-ix/donut-base, specific purpose not explicitly stated