A curated list of resources for Learning with Noisy Labels

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

A Python package for segmenting geospatial data with the Segment Anything Model (SAM)

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Paraformer: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels

Python implementation of the SM2RAIN algorithm

multispectral semantic segmentation network for remote sensing images

A toolbox to make deep learning samples from remote sensing images