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import os
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = "https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4"
_LICENSE = "Public Domain"
_CITATION = """\
"""
_CATEGORIES = ['converse', 'adidas', 'nike']
class SHOECLASSIFICATIONConfig(datasets.BuilderConfig):
"""Builder Config for shoe-classification"""
def __init__(self, data_urls, **kwargs):
"""
BuilderConfig for shoe-classification.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super(SHOECLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_urls = data_urls
class SHOECLASSIFICATION(datasets.GeneratorBasedBuilder):
"""shoe-classification image classification dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
SHOECLASSIFICATIONConfig(
name="full",
description="Full version of shoe-classification dataset.",
data_urls={
"train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/test.zip",
}
,
),
SHOECLASSIFICATIONConfig(
name="mini",
description="Mini version of shoe-classification dataset.",
data_urls={
"train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
"validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
"test": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
},
)
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names=_CATEGORIES),
}
),
supervised_keys=("image", "labels"),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
task_templates=[ImageClassification(image_column="image", label_column="labels")],
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(self.config.data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_files([data_files["train"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"files": dl_manager.iter_files([data_files["validation"]]),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_files([data_files["test"]]),
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
yield i, {
"image_file_path": path,
"image": path,
"labels": os.path.basename(os.path.dirname(path)),
}
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