# Small Window Attention

This model is a fine-tuned image classification model based on the Swin Transformer architecture, achieving 90% accuracy on the evaluation set.

An image classification model based on the Swin Transformer Tiny architecture, fine-tuned on the CIFAR10 dataset with an accuracy of 97.24%

Vision model based on Swin Transformer Tiny architecture, fine-tuned for 10 epochs on image classification tasks

A fine-tuned model based on Swin Transformer architecture, specifically designed for skin cancer image classification tasks

A tiny image classification model based on the Swin Transformer architecture, fine-tuned on an image folder dataset

Image classification model fine-tuned on the CIFAR10 dataset based on Swin Transformer Tiny architecture

Image classification model based on Swin Transformer Tiny architecture, fine-tuned for 130 epochs on a specific image dataset

This model is a fine-tuned version based on the Swin Transformer architecture, specifically designed for MRI image classification tasks, achieving an accuracy of 98.07% on the evaluation set.

This is a tiny model based on the Swin Transformer architecture, specifically designed for image classification tasks and fine-tuned on the EuroSAT dataset.