Datasets:

frgfm
/

imagenette

	# Copyright (C) 2022, François-Guillaume Fernandez.

	# This program is licensed under the Apache License 2.0.
	# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.

	"""Imagenette dataset."""

	import os
	import json

	import datasets


	_HOMEPAGE = "https://github.com/fastai/imagenette"

	_LICENSE = "Apache License 2.0"

	_CITATION = """\
	@software{Howard_Imagenette_2019,
	title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
	author={Jeremy Howard},
	year={2019},
	month={March},
	publisher = {GitHub},
	url = {https://github.com/fastai/imagenette}
	}
	"""

	_DESCRIPTION = """\
	Imagenette is a subset of 10 easily classified classes from Imagenet
	(tench, English springer, cassette player, chain saw, church, French
	horn, garbage truck, gas pump, golf ball, parachute).
	"""

	_LABEL_MAP = [
	'n01440764',
	'n02102040',
	'n02979186',
	'n03000684',
	'n03028079',
	'n03394916',
	'n03417042',
	'n03425413',
	'n03445777',
	'n03888257',
	]

	_REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata"


	class ImagenetteConfig(datasets.BuilderConfig):
	"""BuilderConfig for Imagette."""

	def __init__(self, data_url, metadata_urls, **kwargs):
	"""BuilderConfig for Imagette.
	Args:
	data_url: `string`, url to download the zip file from.
	matadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ImagenetteConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
	self.data_url = data_url
	self.metadata_urls = metadata_urls


	class Imagenette(datasets.GeneratorBasedBuilder):
	"""Imagenette dataset."""

	BUILDER_CONFIGS = [
	ImagenetteConfig(
	name="full_size",
	description="All images are in their original size.",
	data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz",
	metadata_urls={
	"train": f"{_REPO}/imagenette2/train.txt",
	"validation": f"{_REPO}/imagenette2/val.txt",
	},
	),
	ImagenetteConfig(
	name="320px",
	description="All images were resized on their shortest side to 320 pixels.",
	data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz",
	metadata_urls={
	"train": f"{_REPO}/imagenette2-320/train.txt",
	"validation": f"{_REPO}/imagenette2-320/val.txt",
	},
	),
	ImagenetteConfig(
	name="160px",
	description="All images were resized on their shortest side to 160 pixels.",
	data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz",
	metadata_urls={
	"train": f"{_REPO}/imagenette2-160/train.txt",
	"validation": f"{_REPO}/imagenette2-160/val.txt",
	},
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION + self.config.description,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.ClassLabel(
	names=[
	"tench",
	"English springer",
	"cassette player",
	"chain saw",
	"church",
	"French horn",
	"garbage truck",
	"gas pump",
	"golf ball",
	"parachute",
	]
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive_path = dl_manager.download(self.config.data_url)
	metadata_paths = dl_manager.download(self.config.metadata_urls)
	archive_iter = dl_manager.iter_archive(archive_path)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"images": archive_iter,
	"metadata_path": metadata_paths["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"images": archive_iter,
	"metadata_path": metadata_paths["validation"],
	},
	),
	]

	def _generate_examples(self, images, metadata_path):
	with open(metadata_path, encoding="utf-8") as f:
	files_to_keep = set(f.read().split("\n"))
	idx = 0
	for file_path, file_obj in images:
	if file_path in files_to_keep:
	label = _LABEL_MAP.index(file_path.split("/")[-2])
	yield idx, {
	"image": {"path": file_path, "bytes": file_obj.read()},
	"label": label,
	}
	idx += 1