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DARTS
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DARTS
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# DARTS This repository contains the implementation of the following paper: > **DARTS: Double Attention Reference-based Transformer for Super-resolution**<br> > Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness<br> [[Paper](https://arxiv.org/pdf/2307.08837.pdf)] ## Overview  ## Dependencies and Installation 1. Clone Repo ```bash git clone https://github.com/bia006/DARTS.git ``` 2. Create Conda Environment ```bash conda create --name DARTS python=3.8 conda activate DARTS ``` 3. Install Dependencies ```bash cd DARTS pip install -r requirements.txt ``` ## Dataset Preparation - Train Set: [CUFED Dataset](https://drive.google.com/drive/folders/1hGHy36XcmSZ1LtARWmGL5OK1IUdWJi3I) - Test Set: [WR-SR Dataset](https://drive.google.com/drive/folders/16UKRu-7jgCYcndOlGYBmo5Pp0_Mq71hP?usp=sharing), [CUFED5 Dataset](https://drive.google.com/file/d/1Fa1mopExA9YGG1RxrCZZn7QFTYXLx6ph/view) Please refer to [Datasets.md](datasets/DATASETS.md) for pre-processing and more details. ## Get Started ### Pretrained Models Downloading the pretrained models from this [link](https://drive.google.com/file/d/1VWQH3FFhc8ZqDilVfXzlZS99qaF3om0-/view?usp=sharing) and put them under `mmsr/checkpoints folder`. ### Test We provide quick test code with the pretrained model. 1. Modify the paths to dataset and pretrained model in the following yaml files for configuration. ```bash ./options/test/test_DARTS.yml ``` 2. Check out the results in `./results`. ### Train All logging files in the training process, *e.g.*, log message, checkpoints, and snapshots, will be saved to `./mmsr/checkpoints` and `./tb_logger` directory. 1. Modify the paths to dataset in the following yaml files for configuration. ```bash ./options/train/train_DARTS.yml ``` 2. Train the transformer network. ```bash python mmsr/train.py -opt "options/train/train_DATSR.yml" ``` ## Visual Results For more results on the benchmarks, you can directly download our DARTS results from [here](https://drive.google.com/file/d/1Q-wVgMd4ByG5HvY1D9YPv9eWE8aYPUuR/view?usp=sharing).  ## Citation If you find our repo useful for your research, please cite our paper. ## License and Acknowledgement This project is open sourced under MIT license. The code framework is mainly modified from [StyleSwin](https://github.com/microsoft/StyleSwin). Please refer to the original repo for more usage and documents. ## Contact If you have any question, please feel free to contact us via `masi.aslahi@usask.ca`.
