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Convert dataset to Parquet

#1
README.md CHANGED
@@ -1,4 +1,10 @@
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  ---
 
 
 
 
 
 
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  dataset_info:
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  features:
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  - name: image
@@ -19,40 +25,55 @@ dataset_info:
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  '9': lake or sea
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  splits:
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  - name: train
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- num_bytes: 3962928
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  num_examples: 21600
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  - name: test
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  num_examples: 2700
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  - name: contrast
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  - name: gaussian_noise
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  num_examples: 2700
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  - name: spatter
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  num_examples: 2700
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- download_size: 114799801
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- dataset_size: 8147488
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- task_categories:
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- - image-classification
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- language:
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- - en
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- size_categories:
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- - 10K<n<100K
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ---
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+ language:
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+ - en
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+ size_categories:
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+ - 10K<n<100K
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+ task_categories:
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+ - image-classification
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  dataset_info:
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  features:
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  - name: image
 
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  '9': lake or sea
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  splits:
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  - name: train
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+ num_bytes: 70654720.8
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  num_examples: 21600
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  - name: test
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  num_examples: 2700
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  - name: contrast
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  num_examples: 2700
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  - name: gaussian_noise
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  - name: impulse_noise
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  - name: jpeg_compression
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  num_examples: 2700
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  - name: motion_blur
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  num_examples: 2700
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  - name: pixelate
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  num_examples: 2700
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  - name: spatter
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  num_examples: 2700
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+ download_size: 98645265
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+ dataset_size: 102804655.0
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+ configs:
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: test
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+ path: data/test-*
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+ - split: contrast
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+ path: data/contrast-*
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+ - split: gaussian_noise
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+ path: data/gaussian_noise-*
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+ - split: impulse_noise
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+ path: data/impulse_noise-*
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+ - split: jpeg_compression
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+ path: data/jpeg_compression-*
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+ - split: motion_blur
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+ path: data/motion_blur-*
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+ - split: pixelate
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+ path: data/pixelate-*
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+ - split: spatter
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+ path: data/spatter-*
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  ---
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eurosat.py DELETED
@@ -1,58 +0,0 @@
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- import datasets
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- from datasets.data_files import DataFilesDict
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- from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig
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-
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- logger = datasets.logging.get_logger(__name__)
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-
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-
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- class EuroSAT(ImageFolder):
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- R"""
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- EuroSAT dataset for image classification.
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- """
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-
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- BUILDER_CONFIG_CLASS = ImageFolderConfig
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- BUILDER_CONFIGS = [
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- ImageFolderConfig(
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- name="default",
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- features=("images", "labels"),
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- data_files=DataFilesDict(
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- {
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- split: f"data/{split}.zip"
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- for split in ["train", "test"]
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- + ["contrast", "gaussian_noise", "impulse_noise", "jpeg_compression", "motion_blur", "pixelate", "spatter"]
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- }
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- ),
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- )
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- ]
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-
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- classnames = [
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- "annual crop land",
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- "forest",
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- "brushland or shrubland",
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- "highway or road",
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- "industrial buildings or commercial buildings",
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- "pasture land",
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- "permanent crop land",
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- "residential buildings or homes or apartments",
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- "river",
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- "lake or sea",
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- ]
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-
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- clip_templates = [
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- lambda c: f"a centered satellite photo of {c}.",
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- lambda c: f"a centered satellite photo of a {c}.",
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- lambda c: f"a centered satellite photo of the {c}.",
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- ]
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- description="EuroSAT dataset for image classification.",
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- features=datasets.Features(
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- {
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- "image": datasets.Image(),
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- "label": datasets.ClassLabel(names=self.classnames),
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- }
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- ),
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- supervised_keys=("image", "label"),
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- task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
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- )