Convert dataset to Parquet (#2)

- Convert dataset to Parquet (c650355549d47a8a8f51b3ccea68bc002c75ad68)
- Delete loading script (4e8b5f69675fbc1e615e3541cf60932dcb11495f)
- Delete data file (9a333eb1b7625b3337fa2ecb9ed1e8e11e57f2e3)
- Delete data file (1032864a38c150b92458d43a300bd6805b00c202)

MyoQuant-SDH-Data.py

@@ -1,130 +0,0 @@
-# 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.
-"""MyoQuant-SDH-Data: The MyoQuant SDH Model Data."""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{Meyer,
-title = {MyoQuant SDH Data},
-author={Corentin Meyer},
-year={2022}
-}
-"""
-_NAMES = ["control", "sick"]
-
-_DESCRIPTION = """\
-This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data"
-
-_LICENSE = "agpl-3.0"
-
-_URLS = {
-    "SDH_16k": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/SDH_16k.zip"
-}
-_METADATA_URL = {
-    "SDH_16k_metadata": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/metadata.jsonl"
-}
-
-
-class SDH_16k(datasets.GeneratorBasedBuilder):
-    """This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-
-    DEFAULT_CONFIG_NAME = "SDH_16k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.ClassLabel(num_classes=2, names=_NAMES),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_URLS)
-        split_metadata_path = dl_manager.download(_METADATA_URL)
-        files_metadata = {}
-        with open(split_metadata_path["SDH_16k_metadata"], encoding="utf-8") as f:
-            for lines in f.read().splitlines():
-                file_json_metdata = json.loads(lines)
-                files_metadata.setdefault(file_json_metdata["split"], []).append(
-                    file_json_metdata
-                )
-        downloaded_files = dl_manager.download_and_extract(archive_path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["validation"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["test"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, download_path, metadata):
-        """Generate images and labels for splits."""
-        for i, single_metdata in enumerate(metadata):
-            img_path = os.path.join(
-                download_path,
-                single_metdata["split"],
-                single_metdata["label"],
-                single_metdata["file_name"],
-            )
-            yield i, {
-                "image": img_path,
-                "label": single_metdata["label"],
-            }

README.md

@@ -1,5 +1,6 @@
 ---
 dataset_info:
+  config_name: SDH_16k
   features:
   - name: image
     dtype: image
@@ -7,46 +8,55 @@ dataset_info:
     dtype:
       class_label:
         names:
-          0: control
-          1: sick
-  config_name: SDH_16k
+          '0': control
+          '1': sick
   splits:
-  - name: test
-    num_bytes: 683067
-    num_examples: 3358
   - name: train
-    num_bytes: 2466024
+    num_bytes: 1400462101.57
     num_examples: 12085
   - name: validation
-    num_bytes: 281243
+    num_bytes: 161799668.032
     num_examples: 1344
-  download_size: 2257836789
-  dataset_size: 3430334
+  - name: test
+    num_bytes: 411212294.084
+    num_examples: 3358
+  download_size: 1411281481
+  dataset_size: 1973474063.6859999
 annotations_creators:
-  - expert-generated
+- expert-generated
 language: []
 language_creators:
-  - expert-generated
+- expert-generated
 license:
-  - agpl-3.0
+- agpl-3.0
 multilinguality: []
 pretty_name: SDH staining muscle fiber histology images used to train MyoQuant model.
 size_categories:
-  - 10K<n<100K
+- 10K<n<100K
 source_datasets:
-  - original
+- original
 tags:
-  - myology
-  - biology
-  - histology
-  - muscle
-  - cells
-  - fibers
-  - myopathy
-  - SDH
-  - myoquant
+- myology
+- biology
+- histology
+- muscle
+- cells
+- fibers
+- myopathy
+- SDH
+- myoquant
 task_categories:
-  - image-classification
+- image-classification
+configs:
+- config_name: SDH_16k
+  data_files:
+  - split: train
+    path: SDH_16k/train-*
+  - split: validation
+    path: SDH_16k/validation-*
+  - split: test
+    path: SDH_16k/test-*
+  default: true
 ---
 
 # Dataset Card for MyoQuant SDH Data

SDH_16k/{SDH_16k.zip → test-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
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-size 1125988525
+oid sha256:d87c02a0b62d71682605870bc4feb337c639f901f1eb2d4c75e80fe3d42c26ed
+size 281330348

SDH_16k/train-00000-of-00003.parquet

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+size 336464906

SDH_16k/train-00001-of-00003.parquet

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SDH_16k/train-00002-of-00003.parquet

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+size 339377396

SDH_16k/validation-00000-of-00001.parquet

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+size 113138360