Datasets:
Tasks:
Image Classification
Languages:
English
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
extended
Tags:
License:
# 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 | |