CBSD68#

class deepinv.datasets.CBSD68(root: str, download: bool = False, transform: Callable | None = None)[source]#

Bases: Dataset

Dataset for CBSBD68.

Color BSD68 dataset for image restoration benchmarks is part of The Berkeley Segmentation Dataset and Benchmark. It is used for measuring image restoration algorithms performance. It contains 68 images.

Raw data file structure:

self.root --- data-00000-of-00001.arrow
           -- dataset_info.json
           -- state.json

This dataset wraps the huggingface version of the dataset. HF source : https://huggingface.co/datasets/deepinv/CBSD68

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

Examples:

Instanciate dataset and download raw data from the Internet

>>> import shutil
>>> from deepinv.datasets import CBSD68
>>> dataset = CBSD68(root="CBSB68", download=True)  # download raw data at root and load dataset
Dataset has been successfully downloaded.
>>> print(dataset.check_dataset_exists())                # check that raw data has been downloaded correctly
True
>>> print(len(dataset))                                  # check that we have 68 images
68
>>> shutil.rmtree("CBSB68")                         # remove raw data from disk

check_dataset_exists() → bool[source]#

Verify that the HuggingFace dataset folder exists and contains the raw data file.

self.root should have the following structure:

self.root --- data-00000-of-00001.arrow
           -- xxx
           -- xxx

This is a soft verification as we don’t check all the files in the folder.