# ImageNet Pre-training

EfficientNet is a mobile-friendly pure convolutional model that uniformly scales depth, width, and resolution dimensions, trained on the ImageNet-1k dataset.

A vision Transformer (ViT) image feature model trained with the self-supervised DINO method, suitable for image classification and feature extraction tasks.

NAT-Mini is a lightweight vision Transformer model based on neighborhood attention mechanism, designed for ImageNet image classification tasks

MobileViT is a lightweight, low-latency vision Transformer model that combines the strengths of CNNs and Transformers, making it suitable for mobile devices.

RegNet model trained on the ImageNet-1k dataset, an efficient vision model designed through neural architecture search

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

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

RegNet is an image classification model designed through neural architecture search, trained on the ImageNet-1k dataset.

RegNet is a vision classification model trained on ImageNet-1k, featuring an efficient network structure designed through Neural Architecture Search (NAS)

A visual attention network model trained on ImageNet-1k, capturing local and long-range relationships through convolution operations

ResNet18 is an image classification model based on deep residual learning, which solves the difficulty of training deep networks through residual connections.

The distilled version of the Efficient Data Image Transformer (DeiT) model was pre-trained and fine-tuned on ImageNet-1k at 224x224 resolution, extracting knowledge from a teacher model through distillation learning.

DeiT is a more efficiently trained Vision Transformer model, pre-trained and fine-tuned on the ImageNet-1k dataset at 224x224 resolution, suitable for image classification tasks.