Datasets:
Tasks:
Other
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
License:
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - found | |
| language: [] | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - unknown | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - other | |
| task_ids: [] | |
| pretty_name: Urban100 | |
| tags: | |
| - other-image-super-resolution | |
| # Dataset Card for Urban100 | |
| ## 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://github.com/jbhuang0604/SelfExSR | |
| - **Repository**: https://huggingface.co/datasets/eugenesiow/Urban100 | |
| - **Paper**: https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html | |
| - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 | |
| ### Dataset Summary | |
| The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. It was first published by [Huang et al. (2015)](https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html) in the paper "Single Image Super-Resolution From Transformed Self-Exemplars". | |
| 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/Urban100', 'bicubic_x2', split='validation') | |
| eval_dataset = EvalDataset(dataset) | |
| model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) | |
| EvalMetrics().evaluate(model, eval_dataset) | |
| ``` | |
| ### Supported Tasks and Leaderboards | |
| The dataset is commonly used for 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 | |
| Not applicable. | |
| ## Dataset Structure | |
| ### Data Instances | |
| An example of `validation` for `bicubic_x2` looks as follows. | |
| ``` | |
| { | |
| "hr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_HR/img_001.png", | |
| "lr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_LR_x2/img_001.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 |validation| | |
| |-------|---:| | |
| |bicubic_x2|100| | |
| |bicubic_x3|100| | |
| |bicubic_x4|100| | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| The authors have created Urban100 containing 100 HR images with a variety of real-world structures. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| The authors constructed this dataset using images from Flickr (under CC license) using keywords such as urban, city, architecture, and structure. | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| No annotations. | |
| #### Who are the annotators? | |
| No annotators. | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## 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 | |
| - **Original Authors**: [Huang et al. (2015)](https://github.com/jbhuang0604/SelfExSR) | |
| ### Licensing Information | |
| The dataset provided uses images from Flikr under the CC (CC-BY-4.0) license. | |
| ### Citation Information | |
| ```bibtex | |
| @InProceedings{Huang_2015_CVPR, | |
| author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra}, | |
| title = {Single Image Super-Resolution From Transformed Self-Exemplars}, | |
| booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| month = {June}, | |
| year = {2015} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset. | |