# Remote sensing image processing

FPN-based image segmentation model with ResNet34 encoder, optimized for GID dataset

An image segmentation model based on the SegFormer-B5 architecture, fine-tuned on larger-sized image datasets, excels at distinguishing between background and branch regions

SegFormer is an efficient semantic segmentation model based on Transformer, with the b0 version being its lightweight implementation.

A fine-tuned version of Google's ViT model on an image folder dataset for image classification tasks, achieving an accuracy of 90.72%.

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