Datasets:
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# 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',
]
class OpenFireConfig(datasets.BuilderConfig):
"""BuilderConfig for OpenFire."""
def __init__(self, data_url, **kwargs):
"""BuilderConfig for OpenFire.
Args:
data_url: `string`, url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super(OpenFireConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_url = data_url
class OpenFire(datasets.GeneratorBasedBuilder):
"""OpenFire dataset."""
BUILDER_CONFIGS = [
OpenFireConfig(
name="full_size",
description="All images are in their original size.",
data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz",
),
OpenFireConfig(
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",
),
OpenFireConfig(
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",
),
]
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):
data_dir = dl_manager.download_and_extract(self.config.data_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"image_folder": os.path.join(data_dir, "train"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"image_folder": os.path.join(data_dir, "val"),
"split": "validation",
},
),
]
def _generate_examples(self, image_folder, split):
idx = 0
for class_idx, class_folder in _LABEL_MAP:
for filepath in os.listdir(class_folder):
yield idx, {"image": os.path.join(image_folder, class_folder, filepath), "label": class_idx}
idx += 1
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