# ImageNet Classification

PVT is a Transformer-based vision model that employs a pyramid structure for image processing, pre-trained on ImageNet-1K, suitable for image classification tasks.

NAT-Base is a Vision Transformer model trained on ImageNet-1K, which uses the neighborhood attention mechanism for image classification.

NAT-Small is a hierarchical vision transformer based on neighborhood attention, designed for image classification tasks.

CvT-13 is a hybrid architecture model combining convolutional neural networks and vision transformers, pre-trained on the ImageNet-1k dataset, suitable for image classification tasks.

A deep residual network model pre-trained on the ImageNet-1k dataset for image classification tasks

RegNet model trained on imagenet-1k, an efficient vision model designed via neural architecture search

RegNet image classification model trained on ImageNet-1k, featuring an efficient network structure designed through neural architecture search

A general-purpose vision perceiver model pre-trained on ImageNet, utilizing convolutional preprocessing and Transformer architecture, supporting image classification tasks

A deep residual network model pre-trained on the ImageNet dataset for image classification tasks.

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