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import datasets
from datasets import ImageClassification
import os
_DESCRIPTION = """
A small rocket images dataset.
"""
_HOMEPAGE = "https://huggingface.co./datasets/MilkCool/rockets"
_CITATION = ""
_LICENSE = "MIT"
_IMAGES_DIR = "train"
_NAMES = "Rockets" #TODO
class Rockets(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=_NAMES),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager):
#TODO
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "train.jsonl"),
"split": "train",
},
)
]
def _generate_examples(self, images, metadata_path):
with open(metadata_path, encoding="utf-8") as f:
files_to_keep = set(f.read().split("\n"))
for file_path, file_obj in images:
if file_path.startswith(_IMAGES_DIR):
if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
label = file_path.split("/")[2]
yield file_path, {
"image": {"path": file_path, "bytes": file_obj.read()},
"label": label,
}
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