.

Author: simon-donike

docs/index.md

@@ -1,14 +1,14 @@
 <img src="https://github.com/ESAOpenSR/opensr-model/blob/main/resources/opensr_logo.png?raw=true" width="250"/>
 
-# SISR-RS-SRGAN
+# OpenSR-SRGAN
 
 | **Runtime** | **Docs / License** | **Tests** |
 |:-----------:|:------------------:|:---------:|
 | ![PythonVersion](https://img.shields.io/badge/Python-v3.10%20%E2%80%93%20v3.12-blue.svg)<br>![PLVersion](https://img.shields.io/badge/PyTorch%20Lightning-v1.x%20%E2%80%93%20v2.x-blue.svg) | [![Docs](https://img.shields.io/badge/docs-mkdocs%20material-brightgreen)](https://srgan.opensr.eu)<br>![License: Apache](https://img.shields.io/badge/license-Apache%20License%202.0-blue) | [![CI](https://github.com/simon-donike/SISR-RS-SRGAN/actions/workflows/ci.yml/badge.svg)](https://github.com/simon-donike/SISR-RS-SRGAN/actions/workflows/ci.yml)<br>[![codecov](https://codecov.io/gh/simon-donike/SISR-RS-SRGAN/graph/badge.svg?token=PWZND7MHRR)](https://codecov.io/gh/simon-donike/SISR-RS-SRGAN) |
 
 ![Super-resolved Sentinel-2 example](assets/6band_banner.png)
 
-SISR-RS-SRGAN is a comprehensive toolkit for training and evaluating super-resolution GANs on remote-sensing imagery. It packages a flexible generator/discriminator zoo, composable perceptual and reconstruction losses, and the training heuristics
+OpenSR-SRGAN is a comprehensive toolkit for training and evaluating super-resolution GANs on remote-sensing imagery. It packages a flexible generator/discriminator zoo, composable perceptual and reconstruction losses, and the training heuristics
 that make adversarial optimisation tractable—generator warm-up phases, learning-rate scheduling, adversarial-weight ramping, and more. All options are driven by concise YAML configuration files so you can explore new architectures or datasets without
 rewriting pipelines.