First version of the IXI2D dataset

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/train.zip filter=lfs diff=lfs merge=lfs -text
+data/valid.zip filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+.DS_Store
+.idea

README.md

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+---
+annotations_creators:
+- no-annotation
+language: []
+language_creators:
+- expert-generated
+license:
+- cc-by-sa-3.0
+multilinguality: []
+pretty_name: IXI-2D
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- medical imaging
+- mri
+- brains
+task_categories:
+- unconditional-image-generation
+task_ids: []
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 111340223
+    num_examples: 25447
+  - name: validation
+    num_bytes: 12463820
+    num_examples: 2828
+  download_size: 133058419
+  dataset_size: 123804043
+---
+
+# Dataset Card for IXI2D
+
+### Dataset Summary
+
+This IXI2D dataset contains around 28000 2D slices extracted from 600 MRI images of healthy subjects. Each MRI volume was skull-stripped, white matter normalized and registered to the *'fsaverage'* template using affine transformation.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## 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
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@iamkzntsv](https://github.com/iamkzntsv) for adding this dataset.

data/train.zip

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+oid sha256:d55cd05eda000426dec632d2e0f4af3fcd20c141fa4f035eb37af0b0e8c31dba
+size 119677698

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ec4bf194ce7d94b206922596c3d43e26f9eefa31bdc4d457c5f74a37a8c9a0d
+size 13380721

ixi2d.py

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+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_HOMEPAGE = 'https://brain-development.org/ixi-dataset/'
+
+_DESCRIPTION = (
+    "This dataset contains around 28000 2D slices extracted from 600 MRI images of healthy subjects."
+    " Each MRI volume was skull-stripped, white matter normalized and registered to the 'fsaverage' template using affine transformation. "
+)
+
+_URLS = {
+    'train': 'data/train.zip',
+    'valid': 'data/valid.zip'
+}
+
+_LICENSE = """\
+LICENSE AGREEMENT
+=================
+ - The IXI-2D dataset consists of images from IXI Dataset [1] which are
+   property of the Biomedical Image Analysis Group, Imperial College London. Any use beyond
+   scientific fair use must be negotiated with the respective picture owners
+   according to the Creative Commons license [2].
+[1] https://brain-development.org/ixi-dataset/
+[2] https://creativecommons.org/licenses/by-sa/3.0/legalcode
+"""
+
+
+class IXI2D(datasets.GeneratorBasedBuilder):
+    """Food-101 Images dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            description=_DESCRIPTION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path['train']),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path['valid']),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images):
+        for file_path, file_obj in images:
+            yield file_path, {
+                "image": {"path": file_path, "bytes": file_obj.read()},
+            }