# ImageNet classification

ResNet-50 is a residual network model pre-trained on ImageNet-1k, using the v1.5 architecture improvement, suitable for image classification tasks.

ResNet model trained on ImageNet-1k, utilizing residual connection structure, supports image classification tasks

ConvNeXT is a pure convolutional model inspired by vision Transformer designs, pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k, outperforming traditional vision Transformers.

BEiT is a Vision Transformer-based model pre-trained on ImageNet-21k through self-supervised learning and fine-tuned on ImageNet-1k for image classification tasks.

BEiT is an image classification model based on Vision Transformer (ViT) architecture, pretrained with self-supervised learning on ImageNet-21k and fine-tuned on ImageNet-1k.