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import gzip
import numpy as np
import datasets
class FashionMNIST(datasets.GeneratorBasedBuilder):
"""Grayscale image classification.
`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.
"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description="Fashion-MNIST is a dataset of Zalando's article images for image classification tasks.",
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=[
"T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"
])
}
),
supervised_keys=("image", "label"),
homepage="https://github.com/zalandoresearch/fashion-mnist",
license="MIT License",
citation="""@article{xiao2017fashion,
title={Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
journal={arXiv preprint arXiv:1708.07747},
year={2017}}"""
)
def _split_generators(self, dl_manager):
urls = {
"train_images": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz",
"train_labels": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz",
"test_images": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz",
"test_labels": "http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz",
}
downloaded_files = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images_path": downloaded_files["train_images"],
"labels_path": downloaded_files["train_labels"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images_path": downloaded_files["test_images"],
"labels_path": downloaded_files["test_labels"],
},
),
]
def _generate_examples(self, images_path, labels_path):
with gzip.open(images_path, "rb") as img_path:
images = np.frombuffer(img_path.read(), dtype=np.uint8, offset=16).reshape(-1, 28, 28)
with gzip.open(labels_path, "rb") as lbl_path:
labels = np.frombuffer(lbl_path.read(), dtype=np.uint8, offset=8)
for idx, (image, label) in enumerate(zip(images, labels)):
yield idx, {"image": image, "label": label}
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