qianchentao9/swingsr

SwinGSR is the official code release for an academic research paper on image super resolution, which is the task of taking a low resolution image and producing a sharper, higher resolution version of it. The paper has been published in a Springer journal, and this repository is meant to let other researchers reproduce its results or build on the method. The model appears to be built using a Swin Transformer style architecture, a type of neural network commonly used in computer vision research, adapted here for the super resolution task. The code supports training the model from scratch and testing it afterward, with separate configuration files for scaling images up by a factor of 2 or by a factor of 4. Training and testing are both run through command line scripts, with settings controlled by YAML configuration files stored in an options folder. Running this project requires Python 3.8, PyTorch 1.8.0, and an NVIDIA GPU with CUDA installed, since deep learning training of this kind is not practical on a regular CPU. The setup process involves cloning the repository, creating a Python environment, and installing dependencies from a requirements file before running the provided training or testing scripts. The README is short and offers no usage examples beyond the basic commands, no description of the dataset format, and no pretrained model downloads. This is a research code drop built on top of an existing project called SwinIR, intended for people already familiar with training image super resolution models, not a beginner friendly tool.