# Self-supervised ViT

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

H-optimus-1 is a pathology foundation model developed by Bioptimus, trained through self-supervised learning for extracting pathological image features.

Virchow is a self-supervised vision Transformer pretrained on 1.5 million whole-slide histopathology images, serving as a slide-level feature extractor for computational pathology downstream tasks.

An image feature model based on Vision Transformer (ViT), trained using the self-supervised DINO method, suitable for image classification and feature extraction tasks.