Datasets:
Tasks:
Image Classification
Languages:
English
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
extended
Tags:
License:
File size: 5,436 Bytes
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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.
"""Imagewoof dataset."""
import os
import json
import datasets
_HOMEPAGE = "https://github.com/fastai/imagenette#imagewoof"
_LICENSE = "Apache License 2.0"
_CITATION = """\
@software{Howard_Imagewoof_2019,
title={Imagewoof: a subset of 10 classes from Imagenet that aren't so easy to classify},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette#imagewoof}
}
"""
_DESCRIPTION = """\
Imagewoof is a subset of 10 classes from Imagenet that aren't so
easy to classify, since they're all dog breeds. The breeds are:
Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu,
English foxhound, Rhodesian ridgeback, Dingo, Golden retriever,
Old English sheepdog.
"""
_LABEL_MAP = [
'n02086240',
'n02087394',
'n02088364',
'n02089973',
'n02093754',
'n02096294',
'n02099601',
'n02105641',
'n02111889',
'n02115641',
]
_REPO = "https://huggingface.co/datasets/frgfm/imagewoof/resolve/main/metadata"
class ImagewoofConfig(datasets.BuilderConfig):
"""BuilderConfig for Imagewoof."""
def __init__(self, data_url, metadata_urls, **kwargs):
"""BuilderConfig for Imagewoof.
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(ImagewoofConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_url = data_url
self.metadata_urls = metadata_urls
class Imagewoof(datasets.GeneratorBasedBuilder):
"""Imagewoof dataset."""
BUILDER_CONFIGS = [
ImagewoofConfig(
name="full_size",
description="All images are in their original size.",
data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2.tgz",
metadata_urls={
"train": f"{_REPO}/imagewoof2/train.txt",
"validation": f"{_REPO}/imagewoof2/val.txt",
},
),
ImagewoofConfig(
name="320px",
description="All images were resized on their shortest side to 320 pixels.",
data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2-320.tgz",
metadata_urls={
"train": f"{_REPO}/imagewoof2-320/train.txt",
"validation": f"{_REPO}/imagewoof2-320/val.txt",
},
),
ImagewoofConfig(
name="160px",
description="All images were resized on their shortest side to 160 pixels.",
data_url="https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2-160.tgz",
metadata_urls={
"train": f"{_REPO}/imagewoof2-160/train.txt",
"validation": f"{_REPO}/imagewoof2-160/val.txt",
},
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(
names=[
"Australian terrier",
"Border terrier",
"Samoyed",
"Beagle",
"Shih-Tzu",
"English foxhound",
"Rhodesian ridgeback",
"Dingo",
"Golden retriever",
"Old English sheepdog",
]
),
}
),
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
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