Datasets:
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import os
from datasets import DatasetInfo, Features, ClassLabel, Image, GeneratorBasedBuilder, Split
class UTM_Dataset(GeneratorBasedBuilder):
"""UTM Dataset organized in train/validation/test splits with subfolders as classes."""
VERSION = "1.0.0"
def _info(self):
"""Returns the dataset metadata, features, and supervised keys."""
return DatasetInfo(
description="UTM Dataset organized in train/validation/test with subfolders as classes",
features=Features(
{
"image": Image(), # images
"label": ClassLabel(names=self._get_class_names()) # labels from subfolder names
}
),
supervised_keys=("image", "label"),
)
def _get_class_names(self):
"""Get class names from the train folder subdirectories."""
train_dir = os.path.join(self.config.data_dir, "train")
return sorted(
[d for d in os.listdir(train_dir) if os.path.isdir(os.path.join(train_dir, d))]
)
def _split_generators(self, dl_manager):
"""Defines the splits and their corresponding folders."""
data_dir = self.config.data_dir
return [
Split.TRAIN: self._generate_examples(os.path.join(data_dir, "train")),
Split.VALIDATION: self._generate_examples(os.path.join(data_dir, "validation")),
Split.TEST: self._generate_examples(os.path.join(data_dir, "test")),
]
def _generate_examples(self, path):
"""Yields (id, example) tuples for each image in the folder."""
for class_name in sorted(os.listdir(path)):
class_dir = os.path.join(path, class_name)
if not os.path.isdir(class_dir):
continue
for fname in sorted(os.listdir(class_dir)):
if fname.lower().endswith((".png", ".jpg", ".jpeg")):
# Unique id for each example
yield f"{class_name}_{fname}", {
"image": os.path.join(class_dir, fname),
"label": class_name,
}
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