README.md

metadata

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

Dataset Card for PIRM

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/roimehrez/PIRM2018
• Repository: https://huggingface.co/datasets/eugenesiow/PIRM
• Paper: https://arxiv.org/abs/1809.07517
• Leaderboard: https://github.com/eugenesiow/super-image#scale-x2

Dataset Summary

The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution.

This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.

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/PIRM', '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/PIRM_valid_HR/1.png",
    "lr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_LR_x2/1.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	test
bicubic_x2	100	100
bicubic_x3	100	100
bicubic_x4	100	100
unknown_x4	100	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

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: Blau et al. (2018)

Licensing Information

This dataset is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation Information

@misc{blau20192018,
    title={The 2018 PIRM Challenge on Perceptual Image Super-resolution}, 
    author={Yochai Blau and Roey Mechrez and Radu Timofte and Tomer Michaeli and Lihi Zelnik-Manor},
    year={2019},
    eprint={1809.07517},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Contributions

Thanks to @eugenesiow for adding this dataset.