# Swin Transformer

This model is a brain tumor image classification model based on the Swin Transformer architecture, achieving outstanding performance in image classification tasks with an accuracy rate of 99.49%.

UperNet is a framework for semantic segmentation that uses Swin Transformer as the backbone network, enabling efficient pixel-level semantic annotation.

A fine-tuned model based on Swin Transformer architecture for garbage classification tasks, achieving 88.27% accuracy

Image classification model fine-tuned on CIFAR-100 dataset based on Swin Transformer Tiny architecture

A vision model based on Swin Transformer Tiny architecture, fine-tuned on the EuroSAT dataset for image classification tasks

An image classification model based on the Swin Transformer architecture, fine-tuned for 20 epochs on the memes dataset with a validation accuracy of 84.78%

An image classification model fine-tuned on the Food101 dataset based on the Swin Transformer architecture, achieving an accuracy of 92.14%

A food image classification model fine-tuned based on the Swin Transformer architecture, achieving 92.1% accuracy on the Food101 dataset

A snack image classification model based on the Swin Transformer architecture, achieving an accuracy of 94.55% after fine-tuning on a snack dataset

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

This is a fine-tuned model based on the Swin Transformer Tiny architecture, specifically designed for image classification tasks, achieving an accuracy of 97.59% on the evaluation set.

This is a micro image classification model based on the Swin Transformer architecture, specifically fine-tuned for plant health diagnosis tasks, achieving 99.83% accuracy on the evaluation set.

A lightweight image classification model based on Microsoft's Swin Transformer Tiny architecture, achieving 92.86% test accuracy after fine-tuning on a snack classification dataset

A fine-tuned image classification model based on the Swin Transformer architecture, achieving 97.44% accuracy on the image folder dataset