# Image classification backbone

A vision Transformer model trained with DINOv2, improved by adding register tokens to enhance attention mechanism, eliminate artifacts, and boost performance

RegNetY-32GF feature extraction model, pretrained on 2 billion random web images using SEER method, suitable for image classification and feature extraction tasks

A self-supervised feature representation model based on ConvNeXt-V2, pre-trained using the Fully Convolutional Masked Autoencoder (FCMAE) framework

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