46 lines
2.5 KiB
Markdown
46 lines
2.5 KiB
Markdown
# MMSR
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MMSR is an open source image and video super-resolution toolbox based on PyTorch. It is a part of the [open-mmlab](https://github.com/open-mmlab) project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk). MMSR is based on our previous projects: [BasicSR](https://github.com/xinntao/BasicSR), [ESRGAN](https://github.com/xinntao/ESRGAN), and [EDVR](https://github.com/xinntao/EDVR).
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### Highlights
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- **A unified framework** suitable for image and video super-resolution tasks. It is also easy to adapt to other restoration tasks, e.g., deblurring, denoising, etc.
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- **State of the art**: It includes several winning methods in competitions: such as ESRGAN (PIRM18), EDVR (NTIRE19).
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- **Easy to extend**: It is easy to try new research ideas based on the code base.
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### Updates
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[2019-07-25] MMSR v0.1 is released.
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## Dependencies and Installation
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- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download))
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- [PyTorch >= 1.1](https://pytorch.org)
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- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
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- [Deformable Convolution](https://arxiv.org/abs/1703.06211). We use [mmdetection](https://github.com/open-mmlab/mmdetection)'s dcn implementation. Please first compile it.
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```
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cd ./codes/models/archs/dcn
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python setup.py develop
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```
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- Python packages: `pip install -r requirements.txt`
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## Dataset Preparation
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We use datasets in LDMB format for faster IO speed. Please refer to [DATASETS.md](datasets/DATASETS.md) for more details.
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## Training and Testing
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Please see [wiki- Training and Testing](https://github.com/open-mmlab/mmsr/wiki/Training-and-Testing) for the basic usage, *i.e.,* training and testing.
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## Model Zoo and Baselines
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Results and pre-trained models are available in the [wiki-Model Zoo](https://github.com/open-mmlab/mmsr/wiki/Model-Zoo).
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## Contributing
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We appreciate all contributions. Please refer to [mmdetection](https://github.com/open-mmlab/mmdetection/blob/master/CONTRIBUTING.md) for contributing guideline.
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**Python code style**<br/>
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We adopt [PEP8](https://python.org/dev/peps/pep-0008) as the preferred code style. We use [flake8](http://flake8.pycqa.org/en/latest) as the linter and [yapf](https://github.com/google/yapf) as the formatter. Please upgrade to the latest yapf (>=0.27.0) and refer to the [yapf configuration](.style.yapf) and [flake8 configuration](.flake8).
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> Before you create a PR, make sure that your code lints and is formatted by yapf.
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## License
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This project is released under the Apache 2.0 license.
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