README.md

metadata

annotations_creators:
  - machine-generated
language_creators:
  - found
language: []
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: Urban100
size_categories:
  - unknown
source_datasets:
  - original
task_categories:
  - other
task_ids:
  - other-other-image-super-resolution

Dataset Card for Urban100

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • 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) in the paper "Single Image Super-Resolution From Transformed Self-Exemplars".

Install with pip:

pip install datasets super-image

Evaluate a model with the super-image library:

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 leaderboard for:

• Scale 2
• Scale 3
• Scale 4
• Scale 8

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)

Licensing Information

The dataset provided uses images from Flikr under the CC (CC-BY-4.0) license.

Citation Information

@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 for adding this dataset.