# ImageNet fine-tuning

A self-supervised feature representation model based on ConvNeXt-V2, pre-trained using the Fully Convolutional Masked Autoencoder (FCMAE) framework, suitable for image feature extraction and fine-tuning tasks.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning vision model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

RegNet image classification model trained on ImageNet-1k using self-supervised pretraining and fine-tuning methods

RegNet model trained on imagenet-1k, pre-trained in a self-supervised manner on billions of random web images before fine-tuning

This Vision Transformer (ViT) model is pre-trained on the ImageNet-21k dataset and then fine-tuned on the ImageNet dataset, suitable for image classification tasks.

Vision Transformer (ViT) is an image classification model based on the Transformer architecture, achieving efficient image recognition capabilities through pre-training and fine-tuning on the ImageNet-21k and ImageNet datasets.

BEiT is a vision Transformer-based image classification model, pre-trained in a self-supervised manner on ImageNet-21k and fine-tuned on ImageNet-1k.

BEiT is a vision model based on image transformers, employing a BERT-like self-supervised pre-training method. It is first pre-trained and fine-tuned on ImageNet-22k, then further fine-tuned on ImageNet-1k.

Vision Transformer (ViT) is an image classification model based on the Transformer architecture, pre-trained on ImageNet-21k and fine-tuned on ImageNet.

Vision Transformer (ViT) is an image classification model based on the transformer architecture, pre-trained on ImageNet-21k and fine-tuned on ImageNet.