Update parquet files

.gitattributes

@@ -1,16 +0,0 @@
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tar.gz filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text

Div2k.py

@@ -1,157 +0,0 @@
-# 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.
-"""DIV2K dataset: DIVerse 2K resolution high quality images.
-Adapted from TF Datasets: https://github.com/tensorflow/datasets/"""
-
-import os
-from pathlib import Path
-
-import datasets
-
-_CITATION = """
-@InProceedings{Agustsson_2017_CVPR_Workshops,
-author = {Agustsson, Eirikur and Timofte, Radu},
-title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
-booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
-url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
-month = {July},
-year = {2017}
-} 
-"""
-
-_DESCRIPTION = """
-DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and 
-CVPR 2018) and @ PIRM (ECCV 2018)
-"""
-
-_HOMEPAGE = "https://data.vision.ee.ethz.ch/cvl/DIV2K/"
-
-_LICENSE = """
-Please notice that this dataset is made available for academic research purpose only. All the images are
-collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to 
-you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset 
-immediately.
-"""
-
-_DL_URL = "https://data.vision.ee.ethz.ch/cvl/DIV2K/"
-
-_DL_URLS = {
-    "train_hr": _DL_URL + "DIV2K_train_HR.zip",
-    "valid_hr": _DL_URL + "DIV2K_valid_HR.zip",
-    "train_bicubic_x2": _DL_URL + "DIV2K_train_LR_bicubic_X2.zip",
-    "train_unknown_x2": _DL_URL + "DIV2K_train_LR_unknown_X2.zip",
-    "valid_bicubic_x2": _DL_URL + "DIV2K_valid_LR_bicubic_X2.zip",
-    "valid_unknown_x2": _DL_URL + "DIV2K_valid_LR_unknown_X2.zip",
-    "train_bicubic_x3": _DL_URL + "DIV2K_train_LR_bicubic_X3.zip",
-    "train_unknown_x3": _DL_URL + "DIV2K_train_LR_unknown_X3.zip",
-    "valid_bicubic_x3": _DL_URL + "DIV2K_valid_LR_bicubic_X3.zip",
-    "valid_unknown_x3": _DL_URL + "DIV2K_valid_LR_unknown_X3.zip",
-    "train_bicubic_x4": _DL_URL + "DIV2K_train_LR_bicubic_X4.zip",
-    "train_unknown_x4": _DL_URL + "DIV2K_train_LR_unknown_X4.zip",
-    "valid_bicubic_x4": _DL_URL + "DIV2K_valid_LR_bicubic_X4.zip",
-    "valid_unknown_x4": _DL_URL + "DIV2K_valid_LR_unknown_X4.zip",
-    "train_bicubic_x8": _DL_URL + "DIV2K_train_LR_x8.zip",
-    "valid_bicubic_x8": _DL_URL + "DIV2K_valid_LR_x8.zip",
-    "train_realistic_mild_x4": _DL_URL + "DIV2K_train_LR_mild.zip",
-    "valid_realistic_mild_x4": _DL_URL + "DIV2K_valid_LR_mild.zip",
-    "train_realistic_difficult_x4": _DL_URL + "DIV2K_train_LR_difficult.zip",
-    "valid_realistic_difficult_x4": _DL_URL + "DIV2K_valid_LR_difficult.zip",
-    "train_realistic_wild_x4": _DL_URL + "DIV2K_train_LR_wild.zip",
-    "valid_realistic_wild_x4": _DL_URL + "DIV2K_valid_LR_wild.zip",
-}
-
-_DATA_OPTIONS = [
-    "bicubic_x2", "bicubic_x3", "bicubic_x4", "bicubic_x8", "unknown_x2",
-    "unknown_x3", "unknown_x4", "realistic_mild_x4", "realistic_difficult_x4",
-    "realistic_wild_x4"
-]
-
-
-class Div2kConfig(datasets.BuilderConfig):
-    """BuilderConfig for Div2k."""
-
-    def __init__(self, name, **kwargs):
-        """Constructs a Div2kConfig."""
-        if name not in _DATA_OPTIONS:
-            raise ValueError("data must be one of %s" % _DATA_OPTIONS)
-
-        super(Div2kConfig, self).__init__(name=name, **kwargs)
-        self.data = name
-        self.download_urls = {
-            "train_lr_url": _DL_URLS["train_" + self.data],
-            "valid_lr_url": _DL_URLS["valid_" + self.data],
-            "train_hr_url": _DL_URLS["train_hr"],
-            "valid_hr_url": _DL_URLS["valid_hr"],
-        }
-
-
-class Div2k(datasets.GeneratorBasedBuilder):
-    """DIV2K dataset: DIVerse 2K resolution high quality images."""
-
-    BUILDER_CONFIGS = [
-        Div2kConfig(version=datasets.Version("2.0.0"), name=data) for data in _DATA_OPTIONS
-    ]
-
-    DEFAULT_CONFIG_NAME = "bicubic_x2"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "lr": datasets.Value("string"),
-                "hr": 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."""
-        extracted_paths = dl_manager.download_and_extract(
-            self.config.download_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "lr_path": extracted_paths["train_lr_url"],
-                    "hr_path": os.path.join(extracted_paths["train_hr_url"], "DIV2K_train_HR"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "lr_path": extracted_paths["valid_lr_url"],
-                    "hr_path": str(os.path.join(extracted_paths["valid_hr_url"], "DIV2K_valid_HR")),
-                },
-            ),
-        ]
-
-    def _generate_examples(
-            self, lr_path, hr_path
-    ):
-        """ Yields examples as (key, example) tuples. """
-        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) / f"{str(file_path.name)[:4]}.png").as_posix())
-                }

README.md

@@ -1,229 +0,0 @@
----
-annotations_creators:
-- machine-generated
-language_creators:
-- found
-language: []
-license:
-- other
-multilinguality:
-- monolingual
-size_categories:
-- unknown
-source_datasets:
-- original
-task_categories:
-- other
-task_ids: []
-pretty_name: Div2k
-tags:
-- other-image-super-resolution
----
-
-# Dataset Card for Div2k
-
-## 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://data.vision.ee.ethz.ch/cvl/DIV2K/
-- **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k
-- **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf
-- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
-
-### Dataset Summary
-
-DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents.
-
-The DIV2K dataset is divided into:
-
-- train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors
-- validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts.
-
-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/Div2k', '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 training and 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 `train` for `bicubic_x2` looks as follows.
-```
-{
-    "hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png",
-    "lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.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  |train |validation|
-|-------|-----:|---:|
-|bicubic_x2|800|100|
-|bicubic_x3|800|100|
-|bicubic_x4|800|100|
-|bicubic_x8|800|100|
-|unknown_x2|800|100|
-|unknown_x3|800|100|
-|unknown_x4|800|100|
-|realistic_mild_x4|800|100|
-|realistic_difficult_x4|800|100|
-|realistic_wild_x4|800|100|
-
-
-## Dataset Creation
-
-### Curation Rationale
-
-Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-**Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of 
-the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most 
-common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of 
-12 pixels on both axes. Most of the crawled images were originally above 20M pixels. 
-The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions 
-(like blur and color shifts).
-
-**Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely 
-shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr, 
-Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity
-for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments 
-(cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions.
-
-**Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG 
-compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with 
-bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings. 
-
-The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good 
-balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per 
-image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of 
-bicubic, ANR and A+ methods. 
-
-Only the 800 train and 100 validation images are included in this dataset.
-
-#### Who are the source language producers?
-
-The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality,
-to the diversity of sources (sites and cameras), to the image contents and to the copyrights. 
-
-### Annotations
-
-#### Annotation process
-
-No annotations.
-
-#### Who are the annotators?
-
-No annotators.
-
-### Personal and Sensitive Information
-
-All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images 
-belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset 
-immediately.
-
-## 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 Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/)
-
-### Licensing Information
-
-Please notice that this dataset is made available for academic research purpose only. All the images are
-collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to 
-you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset 
-immediately.
-
-### Citation Information
-
-```bibtex
-@InProceedings{Agustsson_2017_CVPR_Workshops,
-    author = {Agustsson, Eirikur and Timofte, Radu},
-    title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
-    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
-    url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
-    month = {July},
-    year = {2017}
-} 
-```
-
-### Contributions
-
-Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.