Set5HR

class deepinv.datasets.Set5HR(root=None, download=False, transform=None, verbose=True, use_dict_output=False)

Bases: ImageFolder

Dataset for Set5.

The Set5 dataset [1] is a dataset consisting of 5 images commonly used for testing performance of image reconstruction algorithms. Images have sizes ranging from 256×256 to 512×512 pixels.

Raw data file structure:

self.root --- Set5_HR.tar.gz
        |
        --- Set5_HR --- baby.png
        |             |
        |             --- bird.png
        |             --- butterfly.png
        |             --- ...
        |
        --- xxx

Raw dataset source : https://huggingface.co/datasets/eugenesiow/Set5

Parameters:

• root (str) – Root directory of dataset. Directory path from where we load and save the dataset.

• download (bool) – If True, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. Default at False.

• transform: (Callable) – (optional) A function/transform that takes in a PIL image and returns a transformed version. E.g, torchvision.transforms.RandomCrop

• verbose (bool) – Print a message if the dataset has been correctly downloaded. Default True.

• use_dict_output (bool) – whether to return output as dict with keys “x”, “y”, “params” instead of tuple (default False).

---

References:

[1]

Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie-Line Alberi-Morel. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In Proceedings of the British Machine Vision Conference (BMVC), 135.1–135.10. 2012.

check_dataset_exists()

Verify that the image folders exist and contain all the images.

self.root should have the following structure:

self.root --- Set5_HR --- baby.png
        |             |
        |             --- bird.png
        |             --- butterfly.png
        |             --- ...
        |
        --- xxx