Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
License:
File size: 1,785 Bytes
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import os
from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator, Value, Image, ClassLabel
_CATEGORIES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
class IntelImageClassification(GeneratorBasedBuilder):
def _info(self):
return DatasetInfo(
description="Intel Image Classification dataset with 6 natural scene categories.",
features={
"image": Image(),
"label": ClassLabel(names=_CATEGORIES),
},
supervised_keys=("image", "label"),
homepage="https://www.kaggle.com/datasets/puneet6060/intel-image-classification",
# citation="Puneet Jindal, Intel Image Classification (Kaggle Dataset)",
)
def _split_generators(self, dl_manager):
data_dir = os.path.join(dl_manager.manual_dir, "data")
return [
SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "seg_train")}),
SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "seg_test")}),
SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "seg_pred")}),
]
def _generate_examples(self, filepath):
idx = 0
for label in sorted(os.listdir(filepath)):
label_path = os.path.join(filepath, label)
if not os.path.isdir(label_path):
continue
for img_file in sorted(os.listdir(label_path)):
if img_file.lower().endswith((".jpg", ".jpeg", ".png")):
yield idx, {
"image": os.path.join(label_path, img_file),
"label": label,
}
idx += 1
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