Note
This is a Hugging Face dataset. Learn how to load datasets from the Hub in the Hugging Face integration docs.
Dataset Card for Set5#
This is a FiftyOne dataset with 135 samples.
Installation#
If you haven’t already, install FiftyOne:
pip install -U fiftyone
Usage#
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Set5")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details#
Dataset Description#
The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models.
Curated by: Bevilacqua, Marco and Roumy, Antoine and Guillemot, Christine and Alberi-Morel, Marie-Line
Language(s) (NLP): en
License: other
Dataset Sources#
Repository: https://github.com/ChaofWang/Awesome-Super-Resolution/blob/master/dataset.md
Paper: Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding
Homepage: https://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html
Uses#
Super-resolution
Dataset Creation#
Citation#
BibTeX:
@inproceedings{bevilacqua2012low,
title={Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding},
author={Bevilacqua, Marco and Roumy, Antoine and Guillemot, Christine and Alberi-Morel, Marie-Line},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
year={2012}
}