Datasets:
BSD100

Task Categories: other
Multilinguality: monolingual
Size Categories: unknown
Language Creators: found
Annotations Creators: machine-generated
Source Datasets: original
Eugene Siow commited on
Commit
600666e
1 Parent(s): 6ac4e0a
BSD100.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """BSD100 dataset: An evaluation dataset for the image super resolution task"""
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+
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+
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+ import datasets
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+ from pathlib import Path
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+
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+
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+ _CITATION = """
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+ @inproceedings{martin2001database,
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+ title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
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+ author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
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+ booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
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+ volume={2},
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+ pages={416--423},
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+ year={2001},
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+ organization={IEEE}
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+ }
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+ """
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+
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+ _DESCRIPTION = """
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+ BSD is a dataset used frequently for image denoising and super-resolution.
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+ BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
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+ """
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+
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+ _HOMEPAGE = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/"
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+
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+ _LICENSE = "UNK"
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+
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+ _DL_URL = "https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/data/"
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+
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+ _DEFAULT_CONFIG = "bicubic_x2"
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+
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+ _DATA_OPTIONS = {
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+ "bicubic_x2": {
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+ "hr": _DL_URL + "BSD100_HR.tar.gz",
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+ "lr": _DL_URL + "BSD100_LR_x2.tar.gz",
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+ },
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+ "bicubic_x3": {
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+ "hr": _DL_URL + "BSD100_HR.tar.gz",
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+ "lr": _DL_URL + "BSD100_LR_x3.tar.gz",
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+ },
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+ "bicubic_x4": {
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+ "hr": _DL_URL + "BSD100_HR.tar.gz",
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+ "lr": _DL_URL + "BSD100_LR_x4.tar.gz",
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+ }
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+ }
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+
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+
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+ class Bsd100Config(datasets.BuilderConfig):
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+ """BuilderConfig for BSD100."""
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+
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+ def __init__(
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+ self,
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+ name,
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+ hr_url,
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+ lr_url,
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+ **kwargs,
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+ ):
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+ if name not in _DATA_OPTIONS:
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+ raise ValueError("data must be one of %s" % _DATA_OPTIONS)
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+ super(Bsd100Config, self).__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
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+ self.hr_url = hr_url
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+ self.lr_url = lr_url
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+
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+
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+ class Bsd100(datasets.GeneratorBasedBuilder):
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+ """BSD100 dataset for single image super resolution evaluation."""
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+
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+ BUILDER_CONFIGS = [
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+ Bsd100Config(
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+ name=key,
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+ hr_url=values['hr'],
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+ lr_url=values['lr']
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+ ) for key, values in _DATA_OPTIONS.items()
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+ ]
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+
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+ DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "hr": datasets.Value("string"),
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+ "lr": datasets.Value("string"),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ hr_data_dir = dl_manager.download_and_extract(self.config.hr_url)
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+ lr_data_dir = dl_manager.download_and_extract(self.config.lr_url)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "lr_path": lr_data_dir,
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+ "hr_path": str(Path(hr_data_dir) / 'BSD100_HR')
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+ },
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+ )
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+ ]
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+
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+ def _generate_examples(
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+ self, hr_path, lr_path
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+ ):
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is here for legacy reason (tfds) and is not important in itself.
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+ extensions = {'.png'}
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+ for file_path in sorted(Path(lr_path).glob("**/*")):
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+ if file_path.suffix in extensions:
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+ file_path_str = str(file_path.as_posix())
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+ yield file_path_str, {
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+ 'lr': file_path_str,
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+ 'hr': str((Path(hr_path) / file_path.name).as_posix())
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+ }
README.md ADDED
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+ ---
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+ annotations_creators:
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+ - machine-generated
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+ language_creators:
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+ - found
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+ languages: []
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+ licenses:
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+ - other-academic-use
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+ multilinguality:
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+ - monolingual
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+ pretty_name: BSD100
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+ size_categories:
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+ - unknown
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - other
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+ task_ids:
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+ - other-other-image-super-resolution
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+ ---
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+
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+ # Dataset Card for BSD100
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+
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+ ## Table of Contents
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+ - [Table of Contents](#table-of-contents)
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+ - [Contributions](#contributions)
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+
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+ ## Dataset Description
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+
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+ - **Homepage**: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
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+ - **Repository**: https://huggingface.co/datasets/eugenesiow/BSD100
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+ - **Paper**: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655
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+ - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
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+
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+ ### Dataset Summary
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+
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+ BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655). The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
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+
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+ Install with `pip`:
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+ ```bash
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+ pip install datasets super-image
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+ ```
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+
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+ Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
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+ ```python
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+ from datasets import load_dataset
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+ from super_image import EdsrModel
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+ from super_image.data import EvalDataset, EvalMetrics
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+
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+ dataset = load_dataset('eugenesiow/BSD100', 'bicubic_x2', split='validation')
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+ eval_dataset = EvalDataset(dataset)
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+ model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
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+ EvalMetrics().evaluate(model, eval_dataset)
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+ ```
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ The dataset is commonly used for evaluation of the `image-super-resolution` task.
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+
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+ Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
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+ - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
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+ - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
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+ - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
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+ - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
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+
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+ ### Languages
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+
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+ Not applicable.
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ An example of `validation` for `bicubic_x2` looks as follows.
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+ ```
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+ {
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+ "hr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_HR/3096.png",
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+ "lr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_LR_x2/3096.png"
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ The data fields are the same among all splits.
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+
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+ - `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
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+ - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
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+
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+ ### Data Splits
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+
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+ | name |validation|
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+ |-------|---:|
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+ |bicubic_x2|100|
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+ |bicubic_x3|100|
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+ |bicubic_x4|100|
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+
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ No annotations.
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+
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+ #### Who are the annotators?
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+
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+ No annotators.
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
151
+ ### Social Impact of Dataset
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+
153
+ [More Information Needed]
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+
155
+ ### Discussion of Biases
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+
157
+ [More Information Needed]
158
+
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+ ### Other Known Limitations
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+
161
+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ - **Original Authors**: [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655)
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+
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+ ### Licensing Information
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+
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+ You are free to download a portion of the dataset for non-commercial research and educational purposes.
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+ In exchange, we request only that you make available to us the results of running your segmentation or
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+ boundary detection algorithm on the test set as described below. Work based on the dataset should cite
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+ the [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655) paper.
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+
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+ ### Citation Information
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+
178
+ ```bibtex
179
+ @inproceedings{martin2001database,
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+ title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
181
+ author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
182
+ booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
183
+ volume={2},
184
+ pages={416--423},
185
+ year={2001},
186
+ organization={IEEE}
187
+ }
188
+ ```
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+
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+ ### Contributions
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+
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+ Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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