Update parquet files

.gitignore

@@ -1,160 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/

A-MNIST.py

@@ -1,93 +0,0 @@
-"""Augmented MNIST Data Set"""
-
-
-import struct
-
-import numpy as np
-
-import datasets
-from datasets.tasks import ImageClassification
-
-_DESCRIPTION = """\
-The dataset is built on top of MNIST.
-It consists from 130K of images in 10 classes - 120K training and 10K test samples.
-The training set was augmented with additional 60K images.
-"""
-
-_URLS = {
-    "train_images": "data/train-images-idx3-ubyte.gz",
-    "train_labels": "data/train-labels-idx1-ubyte.gz",
-    "test_images": "data/t10k-images-idx3-ubyte.gz",
-    "test_labels": "data/t10k-labels-idx1-ubyte.gz",
-}
-
-
-class AMNIST(datasets.GeneratorBasedBuilder):
-    """A-MNIST Data Set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="amnist",
-            version=datasets.Version("1.1.0"),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            task_templates=[
-                ImageClassification(
-                    image_column="image",
-                    label_column="label",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": [downloaded_files["train_images"],
-                                 downloaded_files["train_labels"]],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": [downloaded_files["test_images"],
-                                 downloaded_files["test_labels"]],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """This function returns the examples in the raw form."""
-        # Images
-        with open(filepath[0], "rb") as f:
-            # First 16 bytes contain some metadata
-            _ = f.read(4)
-            size = struct.unpack(">I", f.read(4))[0]
-            _ = f.read(8)
-            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
-
-        # Labels
-        with open(filepath[1], "rb") as f:
-            # First 8 bytes contain some metadata
-            _ = f.read(8)
-            labels = np.frombuffer(f.read(), dtype=np.uint8)
-
-        for idx in range(size):
-            yield idx, {"image": images[idx], "label": str(labels[idx])}

README.md

@@ -1,7 +0,0 @@
----
-license: mit
-task_categories:
-- image-classification
-size_categories:
-- 100K<n<1M
----

data/t10k-labels-idx1-ubyte.gz → amnist/a-mnist-test.parquet

@@ -1,3 +1,3 @@
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-size 4542
+oid sha256:9653e5c55b0a899aef9b7d4900c559c9b322aa22c870585e5a7b13a0db8c4951
+size 2383903

data/train-images-idx3-ubyte.gz → amnist/a-mnist-train.parquet

@@ -1,3 +1,3 @@
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-oid sha256:bc5b5801bd21c495c30aa4ef8219d192a64a19cd00bd410d818cc393fc84bdd6
-size 25600831
+oid sha256:0744bf75f60324f5c0d4004f7044f978eb28dedf47c7363f93d7c001eaffac51
+size 36570077

data/t10k-images-idx3-ubyte.gz

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-oid sha256:8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6
-size 1648877

data/train-labels-idx1-ubyte.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a26c06911ebd3d5998f0b2c3fa4af493cec309f459dc00d6c6aee6ac9adf63b0
-size 56575