Add data.

BSD100.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""BSD100 dataset: An evaluation dataset for the image super resolution task"""
+
+
+import datasets
+from pathlib import Path
+
+
+_CITATION = """
+@inproceedings{martin2001database,
+  title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
+  author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
+  booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
+  volume={2},
+  pages={416--423},
+  year={2001},
+  organization={IEEE}
+}
+"""
+
+_DESCRIPTION = """
+BSD is a dataset used frequently for image denoising and super-resolution. 
+BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
+"""
+
+_HOMEPAGE = "https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/"
+
+_LICENSE = "UNK"
+
+_DL_URL = "https://huggingface.co/datasets/eugenesiow/BSD100/resolve/main/data/"
+
+_DEFAULT_CONFIG = "bicubic_x2"
+
+_DATA_OPTIONS = {
+    "bicubic_x2": {
+        "hr": _DL_URL + "BSD100_HR.tar.gz",
+        "lr": _DL_URL + "BSD100_LR_x2.tar.gz",
+    },
+    "bicubic_x3": {
+        "hr": _DL_URL + "BSD100_HR.tar.gz",
+        "lr": _DL_URL + "BSD100_LR_x3.tar.gz",
+    },
+    "bicubic_x4": {
+        "hr": _DL_URL + "BSD100_HR.tar.gz",
+        "lr": _DL_URL + "BSD100_LR_x4.tar.gz",
+    }
+}
+
+
+class Bsd100Config(datasets.BuilderConfig):
+    """BuilderConfig for BSD100."""
+
+    def __init__(
+        self,
+        name,
+        hr_url,
+        lr_url,
+        **kwargs,
+    ):
+        if name not in _DATA_OPTIONS:
+            raise ValueError("data must be one of %s" % _DATA_OPTIONS)
+        super(Bsd100Config, self).__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
+        self.hr_url = hr_url
+        self.lr_url = lr_url
+
+
+class Bsd100(datasets.GeneratorBasedBuilder):
+    """BSD100 dataset for single image super resolution evaluation."""
+
+    BUILDER_CONFIGS = [
+        Bsd100Config(
+            name=key,
+            hr_url=values['hr'],
+            lr_url=values['lr']
+        ) for key, values in _DATA_OPTIONS.items()
+    ]
+
+    DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "hr": datasets.Value("string"),
+                "lr": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        hr_data_dir = dl_manager.download_and_extract(self.config.hr_url)
+        lr_data_dir = dl_manager.download_and_extract(self.config.lr_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "lr_path": lr_data_dir,
+                    "hr_path": str(Path(hr_data_dir) / 'BSD100_HR')
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self, hr_path, lr_path
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        extensions = {'.png'}
+        for file_path in sorted(Path(lr_path).glob("**/*")):
+            if file_path.suffix in extensions:
+                file_path_str = str(file_path.as_posix())
+                yield file_path_str, {
+                    'lr': file_path_str,
+                    'hr': str((Path(hr_path) / file_path.name).as_posix())
+                }

README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+languages: []
+licenses:
+- other-academic-use
+multilinguality:
+- monolingual
+pretty_name: BSD100
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-image-super-resolution
+---
+
+# Dataset Card for BSD100
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage**: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
+- **Repository**: https://huggingface.co/datasets/eugenesiow/BSD100
+- **Paper**: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655
+- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
+
+### Dataset Summary
+
+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.
+
+Install with `pip`:
+```bash
+pip install datasets super-image
+```
+
+Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
+```python
+from datasets import load_dataset
+from super_image import EdsrModel
+from super_image.data import EvalDataset, EvalMetrics
+
+dataset = load_dataset('eugenesiow/BSD100', 'bicubic_x2', split='validation')
+eval_dataset = EvalDataset(dataset)
+model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
+EvalMetrics().evaluate(model, eval_dataset)
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset is commonly used for evaluation of the `image-super-resolution` task.
+
+Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
+- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
+- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
+- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
+- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
+
+### Languages
+
+Not applicable.
+
+## Dataset Structure
+
+### Data Instances
+
+An example of `validation` for `bicubic_x2` looks as follows.
+```
+{
+    "hr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_HR/3096.png",
+    "lr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_LR_x2/3096.png"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
+- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
+
+### Data Splits
+
+| name  |validation|
+|-------|---:|
+|bicubic_x2|100|
+|bicubic_x3|100|
+|bicubic_x4|100|
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+No annotations.
+
+#### Who are the annotators?
+
+No annotators.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+- **Original Authors**: [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655)
+
+### Licensing Information
+
+You are free to download a portion of the dataset for non-commercial research and educational purposes.
+In exchange, we request only that you make available to us the results of running your segmentation or 
+boundary detection algorithm on the test set as described below. Work based on the dataset should cite 
+the [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655) paper.
+
+### Citation Information
+
+```bibtex
+@inproceedings{martin2001database,
+  title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
+  author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
+  booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
+  volume={2},
+  pages={416--423},
+  year={2001},
+  organization={IEEE}
+}
+```
+
+### Contributions
+
+Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.

data/BSD100_HR.tar.gz

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data/BSD100_LR_x2.tar.gz

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data/BSD100_LR_x4.tar.gz

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