Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 4,833 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
"""FashionMNIST Data Set"""
import struct
import numpy as np
import datasets
from datasets.tasks import ImageClassification
_CITATION = """\
@article{DBLP:journals/corr/abs-1708-07747,
author = {Han Xiao and
Kashif Rasul and
Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms},
journal = {CoRR},
volume = {abs/1708.07747},
year = {2017},
url = {http://arxiv.org/abs/1708.07747},
archivePrefix = {arXiv},
eprint = {1708.07747},
timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
replacement for the original MNIST dataset for benchmarking machine learning algorithms.
It shares the same image size and structure of training and testing splits.
"""
_HOMEPAGE = "https://github.com/zalandoresearch/fashion-mnist"
_LICENSE = "https://raw.githubusercontent.com/zalandoresearch/fashion-mnist/master/LICENSE"
_URL = "https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/"
_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",
}
_NAMES = [
"T - shirt / top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
class FashionMnist(datasets.GeneratorBasedBuilder):
"""FashionMNIST Data Set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="fashion_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=_NAMES),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
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": int(labels[idx])}
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