Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
File size: 3,981 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""MNIST Data Set"""
import struct
import numpy as np
import datasets
from datasets.tasks import ImageClassification
_CITATION = """\
@article{lecun2010mnist,
title={MNIST handwritten digit database},
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
volume={2},
year={2010}
}
"""
_DESCRIPTION = """\
The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
images per class. There are 60,000 training images and 10,000 test images.
"""
_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
_URLS = {
"train_images": "train-images-idx3-ubyte.gz",
"train_labels": "train-labels-idx1-ubyte.gz",
"test_images": "t10k-images-idx3-ubyte.gz",
"test_labels": "t10k-labels-idx1-ubyte.gz",
}
class MNIST(datasets.GeneratorBasedBuilder):
"""MNIST Data Set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="mnist",
version=datasets.Version("1.0.0"),
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"),
homepage="http://yann.lecun.com/exdb/mnist/",
citation=_CITATION,
task_templates=[
ImageClassification(
image_column="image",
label_column="label",
)
],
)
def _split_generators(self, dl_manager):
urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
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])}
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