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BasicSR is an open source image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future).
(ESRGAN, EDVR, DNI, SFTGAN)
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.3
- NVIDIA GPU + CUDA
Please run the following commands in the BasicSR root path to install BasicSR:
(Make sure that your GCC version: gcc >= 5)
pip install -r requirements.txt
python setup.py develop
Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try Windows WSL with CUDA supports :-) (It is now only available for insider build with Fast ring).
We provides simple pipelines to train/test/inference models for quick start. These pipelines/commands cannot cover all the cases and more details are in the following sections.
Please see project boards.
- Please refer to DatasetPreparation.md for more details.
- The descriptions of currently supported datasets (
torch.utils.data.Dataset
classes) are in Datasets.md.
- Training and testing commands: Please see TrainTest.md for the basic usage.
- Options/Configs: Please refer to Config.md.
- Logging: Please refer to Logging.md.
Download official pre-trained models
Download reproduced models and logs
- The descriptions of currently supported models are in Models.md.
- Pre-trained models and log examples are available in ModelZoo.md.
- We also provide training curves in wandb:
Please see DesignConvention.md for the designs and conventions of the BasicSR codebase.
The figure below shows the overall framework. More descriptions for each component:
Datasets.md | Models.md | Config.md | Logging.md
This project is released under the Apache 2.0 license. More details about license and acknowledgement are in LICENSE.
If you have any question, please email [email protected]
.