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Dataset Card for Set5
Dataset Summary
Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. The 5 images of the dataset are (“baby”, “bird”, “butterfly”, “head”, “woman”).
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/Set5', '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/Set5_HR/baby.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Set5_LR_x2/baby.png"
}
Data Fields
The data fields are the same among all splits.
hr
: astring
to the path of the High Resolution (HR).png
image.lr
: astring
to the path of the Low Resolution (LR).png
image.
Data Splits
name | validation |
---|---|
bicubic_x2 | 5 |
bicubic_x3 | 5 |
bicubic_x4 | 5 |
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: Bevilacqua et al.
Licensing Information
Unknown.
Citation Information
@article{bevilacqua2012low,
title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
year={2012},
publisher={BMVA press}
}
Contributions
Thanks to @eugenesiow for adding this dataset.