# Remote sensing image recognition

An image classification model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the EuroSAT dataset for satellite 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.

Vision Transformer model based on ViT architecture, achieving 98.89% accuracy after fine-tuning on image classification tasks

An image classification model fine-tuned based on the ConvNext-base architecture, excelling in land cover classification tasks with an accuracy of 99.19%.

An image classification model based on the ViT architecture, fine-tuned on the image_folder dataset with an accuracy of 90.17%

A fine-tuned image classification model based on Swin Transformer architecture, achieving 93.94% accuracy on the EuroSAT dataset

Fine-tuned image classification model based on Swin Transformer architecture, achieving 98% accuracy on the EuroSAT dataset

This model is a fine-tuned image classification model based on microsoft/swin-tiny-patch4-window7-224, achieving an accuracy of 96.19% on the evaluation dataset.

This model is a fine-tuned version based on the Swin Transformer architecture, specifically designed for image classification tasks, achieving 97.26% accuracy on the EuroSAT dataset.

A fine-tuned image classification model based on Swin Transformer Tiny architecture, achieving 97.26% accuracy 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.

An image classification model fine-tuned on an image folder dataset based on Visual-Attention-Network/van-base, achieving an accuracy of 98.85%

A high-precision remote sensing image classification model fine-tuned on the EuroSAT dataset based on Google's Vision Transformer architecture