# High-resolution image classification

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image processing through hierarchical feature maps and local window self-attention mechanisms.

CvT-w24 is a vision transformer model pre-trained on ImageNet-22k and fine-tuned at 384x384 resolution, improving traditional vision transformers through convolutional enhancements.

CvT-13 is a vision model combining convolution and Transformer, pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k, suitable for image classification tasks.

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.

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.

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.

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.