数据集:
eugenesiow/Set14
Set14 is an evaluation dataset with 14 RGB images for the image super resolution task. It was first used as the test set of the paper "On single image scale-up using sparse-representations" by Zeyde et al. (2010) .
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/Set14', 'bicubic_x2', split='validation') eval_dataset = EvalDataset(dataset) model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) EvalMetrics().evaluate(model, eval_dataset)
The dataset is commonly used for evaluation of the image-super-resolution task.
Unofficial super-image leaderboard for:
Not applicable.
An example of validation for bicubic_x2 looks as follows.
{ "hr": "/.cache/huggingface/datasets/downloads/extracted/Set14_HR/baboon.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/Set14_LR_x2/baboon.png" }
The data fields are the same among all splits.
name | validation |
---|---|
bicubic_x2 | 14 |
bicubic_x3 | 14 |
bicubic_x4 | 14 |
[More Information Needed]
[More Information Needed]
Who are the source language producers?[More Information Needed]
No annotations.
Who are the annotators?No annotators.
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[More Information Needed]
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Academic use only.
@inproceedings{zeyde2010single, title={On single image scale-up using sparse-representations}, author={Zeyde, Roman and Elad, Michael and Protter, Matan}, booktitle={International conference on curves and surfaces}, pages={711--730}, year={2010}, organization={Springer} }
Thanks to @eugenesiow for adding this dataset.