# Swin-Transformer architecture

A tiny model based on Swin Transformer architecture, specifically fine-tuned for CIFAR-10 image classification tasks

This is a tiny version of the image classification model based on the Swin Transformer architecture, fine-tuned on the EuroSAT dataset, suitable for remote sensing image classification tasks.

This model is an image classification model based on the Swin Transformer architecture, achieving 98.9% accuracy after fine-tuning on the CIFAR-10 dataset.

A fine-tuned image classification model based on the Swin Transformer architecture, achieving 98.3% accuracy on the image folder dataset.

This model is an image classification model fine-tuned on the CIFAR-10 dataset based on the Swin Transformer Tiny architecture, achieving an accuracy of 97.89%.