--- annotations_creators: - machine-generated language_creators: - found languages: [] licenses: - other-academic-use multilinguality: - monolingual pretty_name: Div2k size_categories: - unknown source_datasets: - original task_categories: - other task_ids: - other-other-image-super-resolution --- # Dataset Card for Div2k ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage: https://data.vision.ee.ethz.ch/cvl/DIV2K/** - **Repository: https://huggingface.co/datasets/eugenesiow/Div2k** - **Paper: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf** - **Leaderboard: https://github.com/eugenesiow/super-image#scale-x2** - **Point of Contact: radu.timofte@vision.ee.ethz.ch** ### Dataset Summary DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents. The DIV2K dataset is divided into: - train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors - validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts. Install with `pip`: ```bash pip install datasets super-image ``` Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library: ```python from datasets import load_dataset from super_image import EdsrModel from super_image.data import EvalDataset, EvalMetrics dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation') eval_dataset = EvalDataset(dataset) model = EdsrModel.from_pretrained(input_dir, scale=scale) EvalMetrics.evaluate(model, eval_dataset) ``` ### Supported Tasks and Leaderboards The dataset is commonly used for training and evaluation of the `image-super-resolution` task. Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for: - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2) - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3) - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4) - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8) ### Languages [More Information Needed] ## Dataset Structure ### Data Instances An example of `train` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png" } ``` ### Data Fields The data fields are the same among all splits. - `hr`: a `string` to the path of the High Resolution (HR) `.png` image. - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image. ### Data Splits | name |train |validation| |-------|-----:|---:| |bicubic_x2|800|100| |bicubic_x3|800|100| |bicubic_x4|800|100| |bicubic_x8|800|100| |unknown_x2|800|100| |unknown_x3|800|100| |unknown_x4|800|100| |realistic_mild_x4|800|100| |realistic_difficult_x4|800|100| |realistic_wild_x4|800|100| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ### Citation Information ```bibtex @InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.