Convert dataset to Parquet (#1)

- Convert dataset to Parquet (9d4918c46d94543e89b13080d8e89c449085dd16)
- Delete loading script (70d49b86688a630f9d629774ee39e6f3eab047ae)
- Delete data file (8afd7b6aa13da586057d50dfa4f43d8edacc84ee)
- Delete data file (cf423dd16d89c41286358fe0d7e764e383adff54)
- Delete data file (0d0328f70d04f44a07be7a3379ada95e20588be3)
- Delete data file (4aa933e5f0723868d1dcbe2e499335244dfdd6dd)
- Delete data file (68577da212c6b26e5e835614237ca39c9232bdbe)
- Delete data file (9f2ea85865221db7a2aad376b02a6963ab591a04)
- Delete data file (3b93b2369b43b6b5cf91c27f4ca634f2251a2eb2)
- Delete data file (1c6fda2bcfa81062357a725574e1574169bcded2)
- Delete data file (cb2056b8af38b7a916255305c48b436f07c3f9de)

README.md

@@ -1,4 +1,10 @@
 ---
+language:
+- en
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-classification
 dataset_info:
   features:
   - name: image
@@ -19,40 +25,55 @@ dataset_info:
           '9': lake or sea
   splits:
   - name: train
-    num_bytes: 3962928
+    num_bytes: 70654720.8
     num_examples: 21600
   - name: test
-    num_bytes: 489995
+    num_bytes: 8626053.5
     num_examples: 2700
   - name: contrast
-    num_bytes: 511595
+    num_bytes: 2577694.4
     num_examples: 2700
   - name: gaussian_noise
-    num_bytes: 543995
+    num_bytes: 4344552.8
     num_examples: 2700
   - name: impulse_noise
-    num_bytes: 538595
+    num_bytes: 4551961.4
     num_examples: 2700
   - name: jpeg_compression
-    num_bytes: 554795
+    num_bytes: 2879624.6
     num_examples: 2700
   - name: motion_blur
-    num_bytes: 527795
+    num_bytes: 2881150.1
     num_examples: 2700
   - name: pixelate
-    num_bytes: 511595
+    num_bytes: 2114682.2
     num_examples: 2700
   - name: spatter
-    num_bytes: 506195
+    num_bytes: 4174215.2
     num_examples: 2700
-  download_size: 114799801
-  dataset_size: 8147488
-task_categories:
-- image-classification
-language:
-- en
-size_categories:
-- 10K<n<100K
+  download_size: 98645265
+  dataset_size: 102804655.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: contrast
+    path: data/contrast-*
+  - split: gaussian_noise
+    path: data/gaussian_noise-*
+  - split: impulse_noise
+    path: data/impulse_noise-*
+  - split: jpeg_compression
+    path: data/jpeg_compression-*
+  - split: motion_blur
+    path: data/motion_blur-*
+  - split: pixelate
+    path: data/pixelate-*
+  - split: spatter
+    path: data/spatter-*
 ---
 
 

data/{contrast.zip → contrast-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
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-oid sha256:66c8d83d8f0b602313fa7fe6db0ade135990173be1dbd7f0bba617d971d9e867
-size 3117057
+oid sha256:248543ce5fb9ffcfae41af7579240d55d195f93900b2d6f70c1679147bd1c957
+size 1414683

data/{gaussian_noise.zip → gaussian_noise-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:858f0f12b07a068676a2fefd1c0b77af4ac6887e9b1e510ced4c74da8f8a52bb
-size 4962476
+oid sha256:0012b4457effae36ec9ef14874faff8c4b182e08dad16ab691bee071250c532d
+size 3379572

data/{impulse_noise.zip → impulse_noise-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
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-oid sha256:ba070a3e856e3e8102cd6c433a1338a0bfd79d56479905ba55f10c224ef09de8
-size 5155764
+oid sha256:96b6850aee61193386597cf11a2b5cc98b7f70308b67007f35fcefe3b717d6e5
+size 3602617

data/{jpeg_compression.zip → jpeg_compression-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
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-oid sha256:44bccccbc692d3e1df9857e2abcd95cd3ad192b93e31f7818f0ddaf78aa62ffe
-size 3539157
+oid sha256:a803f936b86749b14642ff3dc9e412581af31e780f14c22c0a959151614eec61
+size 1827382

data/motion_blur-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:925da9da5a5fe5a61d1d6de62c3276feeaa58676e9b48ab372237694d2251c8b
+size 1771787

data/motion_blur.zip

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-version https://git-lfs.github.com/spec/v1
-oid sha256:d27fbf4e4f8f123febeb915df4ee65ec67917c4141174413274c4f1e022bb9b9
-size 3465104

data/pixelate-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59c7758bb0369ad9cf717e083ff33c92783f638432170b415b18a5be3062d5ae
+size 765724

data/pixelate.zip

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-version https://git-lfs.github.com/spec/v1
-oid sha256:271334e75cce92eef9a1bd5498cab4ac7031dcb3a38fce61d8871a2831394ada
-size 2503369

data/spatter-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe490a7564bdd93276baa46f8271df0e963d7810f9506187fb56d57e3f5827b9
+size 3215883

data/spatter.zip

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-version https://git-lfs.github.com/spec/v1
-oid sha256:01ca43ad933878014bd283eccfb0d8731c08306aa86acc48120e5c4effd710c2
-size 4767237

data/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:815d6c3b86d277dbc3164961936ba47ea0f4b4bafd59059fe93f7cd473cd5044
+size 9134700

data/test.zip

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-version https://git-lfs.github.com/spec/v1
-oid sha256:16a5ca861583acb87c8b5b5ceeb5dae0b4f712d865bd0368070870f5a168e873
-size 9634576

data/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:27f28503c24d6fd64432c25825de0621853d3b04e2881be78748555289902656
+size 73532917

data/train.zip

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-version https://git-lfs.github.com/spec/v1
-oid sha256:ece9c02ec765c2a916f779d5777d85876258e47a0d9363af444cd6c42182f506
-size 77655061

eurosat.py

@@ -1,58 +0,0 @@
-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")],
-        )