# Self-supervised Vision Transformer

A large-scale image feature extraction model based on Vision Transformer (ViT), pre-trained on the ImageNet-1k dataset using the self-supervised masked autoencoder (MAE) method

Self-supervised image feature extraction model based on Vision Transformer (ViT), trained using the DINO method

A vision Transformer (ViT) image feature model trained with the self-supervised DINO method, suitable for image classification and feature extraction tasks.

BEiT is a self-supervised learning model based on Vision Transformer (ViT), pretrained on the ImageNet-21k dataset for image classification tasks.

BEiT is a Vision Transformer (ViT)-based image classification model, pre-trained in a self-supervised manner on ImageNet-22k and fine-tuned on the same dataset.

BEiT is a vision Transformer-based model pre-trained on the ImageNet-21k dataset through self-supervised learning for image classification tasks.

BEiT is a Vision Transformer (ViT)-based image classification model, pre-trained in a self-supervised manner on ImageNet-22k and fine-tuned on the same dataset.

A Vision Transformer model trained using the DINO self-supervised method, based on the ViT architecture and pretrained on the ImageNet-1k dataset.