Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K<n<10K
License:
File size: 3,591 Bytes
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# Copyright 2022 Cristóbal Alcázar
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Rock Glacier dataset with images of the chilean andes."""
import os
import datasets
from datasets.tasks import ImageClassification
_HOMEPAGE = "https://github.com/alcazar90/rock-glacier-detection"
_CITATION = """\
@ONLINE {rock-glacier-dataset,
author="CMM-Glaciares",
title="Rock Glacier Dataset",
month="October",
year="2022",
url="https://github.com/alcazar90/rock-glacier-detection"
}
"""
_DESCRIPTION = """\
TODO: Add a description...
"""
_MASKS_URLS = ["https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/glaciar_masks_trainset.zip"]
_URLS = {
"train": "https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/alkzar90/rock-glacier-dataset/resolve/main/data/validation.zip"
}
_NAMES = ["glaciar", "cordillera"]
class RockGlacierConfig(datasets.BuilderConfig):
def __init__(self, name, **kwargs):
super(RockGlacierConfig, self).__init__(
version=datasets.Version("1.0.0"),
name=name,
description="Rock Glacier Dataset",
**kwargs,
)
class RockGlacierDataset(datasets.GeneratorBasedBuilder):
"""Rock Glacier images dataset."""
BUILDER_CONFIGS = [
RockGlacierConfig("image-classification"),
RockGlacierConfig("image-segmentation"),
]
def _info(self):
if self.config.name == "image-classification":
features = datasets.Features({
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names=_NAMES),
})
keys = ("image", "labels")
if self.config.name == "image-segmentation":
features = datasets.Features({
"image": datasets.Image(),
"labels": datasets.Image(),
})
keys = ("image", "labels")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=keys,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_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"]]),
},
),
]
def _generate_examples(self, files):
if selg.config.name == "image-classification":
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith(".png"):
yield i, {
"image": path,
"labels": os.path.basename(os.path.dirname(path)).lower(),
}
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