Datasets:
PIRM

Task Categories: other
Multilinguality: monolingual
Size Categories: unknown
Language Creators: found
Annotations Creators: machine-generated
Source Datasets: original

Dataset Card for PIRM

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:

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

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.

Models trained or fine-tuned on eugenesiow/PIRM

None yet