Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
import datasets | |
from datasets.data_files import DataFilesDict | |
from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig | |
logger = datasets.logging.get_logger(__name__) | |
class EuroSAT(ImageFolder): | |
R""" | |
EuroSAT dataset for image classification. | |
""" | |
BUILDER_CONFIG_CLASS = ImageFolderConfig | |
BUILDER_CONFIGS = [ | |
ImageFolderConfig( | |
name="default", | |
features=("images", "labels"), | |
data_files=DataFilesDict( | |
{ | |
split: f"data/{split}.zip" | |
for split in ["train", "test"] | |
+ ["contrast", "gaussian_noise", "impulse_noise", "jpeg_compression", "motion_blur", "pixelate", "spatter"] | |
} | |
), | |
) | |
] | |
classnames = [ | |
"annual crop land", | |
"forest", | |
"brushland or shrubland", | |
"highway or road", | |
"industrial buildings or commercial buildings", | |
"pasture land", | |
"permanent crop land", | |
"residential buildings or homes or apartments", | |
"river", | |
"lake or sea", | |
] | |
clip_templates = [ | |
lambda c: f"a centered satellite photo of {c}.", | |
lambda c: f"a centered satellite photo of a {c}.", | |
lambda c: f"a centered satellite photo of the {c}.", | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description="EuroSAT dataset for image classification.", | |
features=datasets.Features( | |
{ | |
"image": datasets.Image(), | |
"label": datasets.ClassLabel(names=self.classnames), | |
} | |
), | |
supervised_keys=("image", "label"), | |
task_templates=[datasets.ImageClassification(image_column="image", label_column="label")], | |
) | |