MetaCLIP 2 (worldwide) is a multilingual zero-shot image classification model based on the Transformer architecture. It supports visual language understanding tasks globally and can classify images without training.

MultimodalTransformersOther

A 7-billion-parameter visual Transformer model trained on 8 billion MetaCLIP data using the DINOv2 self-supervised learning framework, requiring no language supervision

Computer VisionTransformers

A 224-resolution Vision Transformer model based on 2 billion MetaCLIP data, trained using DINOv2 self-supervised learning method

Computer VisionTransformers

A 3-billion parameter vision Transformer model trained on 2 billion carefully curated MetaCLIP data using DINOv2 self-supervised learning framework

Computer VisionTransformers

A dual-framework compatible vision model trained on MetaCLIP-2.5B dataset, supporting both OpenCLIP and timm frameworks

Computer VisionSafetensors

A dual-purpose vision-language model trained on the MetaCLIP-2.5B dataset, supporting zero-shot image classification tasks

Computer VisionSafetensors

A dual-framework compatible vision model trained on MetaCLIP-2.5B dataset, supporting zero-shot image classification tasks

Computer VisionSafetensors

Vision Transformer model trained on MetaCLIP-400M dataset, supporting zero-shot image classification tasks

Computer VisionSafetensors

A dual-framework compatible vision model trained on the MetaCLIP-2.5B dataset, supporting both OpenCLIP and timm frameworks

Computer VisionSafetensors

A dual-framework compatible vision model trained on the MetaCLIP-400M dataset, supporting both OpenCLIP and timm frameworks

Computer VisionSafetensors

A vision Transformer model trained on the MetaCLIP-2.5B dataset, compatible with both open_clip and timm frameworks

Computer VisionSafetensors

A vision-language model trained on the MetaCLIP-400M dataset, supporting zero-shot image classification tasks

Computer VisionSafetensors

MetaCLIP is a vision-language model trained on CommonCrawl data for constructing shared image-text embedding spaces.

MultimodalTransformers

MetaCLIP is a large-scale vision-language model trained on 2.5 billion data points from CommonCrawl (CC), revealing CLIP's data filtering methodology

MultimodalTransformers

MetaCLIP is a vision-language model trained on CommonCrawl data for constructing shared image-text embedding spaces

MultimodalTransformers

MetaCLIP is an implementation of the CLIP framework applied to CommonCrawl data, aiming to reveal CLIP's training data filtering methods

MultimodalTransformers

MetaCLIP is a vision-language model based on CommonCrawl data, improving CLIP model performance through enhanced data filtering methods

MultimodalTransformers

MetaCLIP is a vision-language model trained on 2.5 billion data points from CommonCrawl (CC) to construct a shared image-text embedding space.

MultimodalTransformers

The MetaCLIP base model is a vision-language model trained on CommonCrawl data for constructing shared image-text embedding spaces.

MultimodalTransformers