Convert dataset to Parquet (#1)

- Convert dataset to Parquet (17e82280162dc14216676b512b32480a36354add)
- Delete data file (99a7cc0d00fd24791daf9af8a06e7bde7e1632b4)
- Delete loading script (477ab1ecb1f7c0fa3d93589f1b1d17e9c6e8cc3f)
- Delete data file (75568953f5fbcf49ef2b37fe62aa576cb5bc966b)
- Delete data file (4b6c302ea0f78a0726169b215aa82d15ebb9bf76)
- Delete data file (5f57eabc536daf71504ebd48eb29fb6a825f93c9)
- Delete data file (352d4d16403428c159e129ec984859765090c5c7)
- Delete data file (e3ffefcda1202b0777f92ff13f87204142b07195)
- Delete data file (9afb29f305321ad8d2b207c60e764471235c5841)
- Delete data file (ba26cb1a543d040b48b938a16a8496dcb3d5433b)
- Delete data file (1e1b8e2c311e5878d88bc08fe41de0b602226617)

README.md

@@ -54,34 +54,55 @@ dataset_info:
           '44': wetland
   splits:
   - name: train
-    num_bytes: 2383019
+    num_bytes: 246710368.7
     num_examples: 18900
   - name: test
-    num_bytes: 781557
+    num_bytes: 87460774.8
     num_examples: 6300
   - name: contrast
-    num_bytes: 831957
+    num_bytes: 67512032.7
     num_examples: 6300
   - name: gaussian_noise
-    num_bytes: 907557
+    num_bytes: 116440617.3
     num_examples: 6300
   - name: impulse_noise
-    num_bytes: 894957
+    num_bytes: 125449913.4
     num_examples: 6300
   - name: jpeg_compression
-    num_bytes: 932757
+    num_bytes: 85196403.6
     num_examples: 6300
   - name: motion_blur
-    num_bytes: 869757
+    num_bytes: 73908158.1
     num_examples: 6300
   - name: pixelate
-    num_bytes: 831957
+    num_bytes: 5573022.0
     num_examples: 6300
   - name: spatter
-    num_bytes: 819357
+    num_bytes: 109007915.1
     num_examples: 6300
-  download_size: 916981012
-  dataset_size: 9252875
+  download_size: 911199338
+  dataset_size: 917259205.7
+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-*
 ---
 
 # [RESISC45](https://www.tensorflow.org/datasets/catalog/resisc45)

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

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-size 65821198
+oid sha256:14521b67381193021fac0bc6cd1e7215c046371b93beeb9ff0e3b86116e2f43f
+size 65486738

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3fbcc81eb278e18e9e8bc75a220d07caccc1c8ac8c55eed2d84c7f8aca804df
-size 115764717
+oid sha256:d4c03081639a93b4c3b451383549548e480bf933609bdf61026a055b4af4e7f8
+size 115717004

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7422b2cde62e6697a3d0ef3f0727aca21b788ab7b45a1622a2b57f4c65c32332
-size 124570638
+oid sha256:32f87d44342109e5582e8edad1faf4a6857035d9ac151da251210d678eaac1bd
+size 124422064

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

@@ -1,3 +1,3 @@
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-size 72616247
+oid sha256:ae8d698122b94189025f812e7544f4ddead8933fc7d3c2923686b22a47cd7861
+size 82891945

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

@@ -1,3 +1,3 @@
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-oid sha256:5e3c4d35e8b22f6db7e50f8394f7cd1e40dbbeec75f4cd92b27b9610b2d585fc
-size 82819474
+oid sha256:9d0a90270bd50a10737ee3deb5ec07a216cdcdfca97c9d1d85471550441401a4
+size 72131353

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:10ec683407ab63d57b7109fa9c78edf7a78b1e83572bfeddd052decee8e4e0c4
-size 5677005
+oid sha256:e4ba9413e418cd3b616e5f8958364470ef2a8c62e3b89b81762b72927d10e660
+size 2375070

data/spatter-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
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+size 108346707

data/spatter.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:99bb1700ff22675d5521d919b59ad26dd6716c3e4d98ecb89fdb2cdef23f827f
-size 108574042

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdd84285a7eb4c45b1cea8f125991c2cdd96c2d24c5f2f873585dcdf4b8fc248
+size 85069793

data/test.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5955d0c47b55a178b30a3b6f5f8dff1447a3d2d6e018a6867ddb3d0e640de32b
-size 85393782

data/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b7238d16d25fbf1d14370ccf8d35e1450a50c98aec7833c544d31f44cb37590
+size 254758664

data/train.zip

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-version https://git-lfs.github.com/spec/v1
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-size 255743909

resisc45.py

@@ -1,108 +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 RESISC45(ImageFolder):
-    R"""
-    RESISC45 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 = [
-        "airplane",
-        "airport",
-        "baseball diamond",
-        "basketball court",
-        "beach",
-        "bridge",
-        "chaparral",
-        "church",
-        "circular farmland",
-        "cloud",
-        "commercial area",
-        "dense residential",
-        "desert",
-        "forest",
-        "freeway",
-        "golf course",
-        "ground track field",
-        "harbor",
-        "industrial area",
-        "intersection",
-        "island",
-        "lake",
-        "meadow",
-        "medium residential",
-        "mobile home park",
-        "mountain",
-        "overpass",
-        "palace",
-        "parking lot",
-        "railway",
-        "railway station",
-        "rectangular farmland",
-        "river",
-        "roundabout",
-        "runway",
-        "sea ice",
-        "ship",
-        "snowberg",
-        "sparse residential",
-        "stadium",
-        "storage tank",
-        "tennis court",
-        "terrace",
-        "thermal power station",
-        "wetland",
-    ]
-
-    clip_templates = [
-        lambda c: f"satellite imagery of {c}.",
-        lambda c: f"aerial imagery of {c}.",
-        lambda c: f"satellite photo of {c}.",
-        lambda c: f"aerial photo of {c}.",
-        lambda c: f"satellite view of {c}.",
-        lambda c: f"aerial view of {c}.",
-        lambda c: f"satellite imagery of a {c}.",
-        lambda c: f"aerial imagery of a {c}.",
-        lambda c: f"satellite photo of a {c}.",
-        lambda c: f"aerial photo of a {c}.",
-        lambda c: f"satellite view of a {c}.",
-        lambda c: f"aerial view of a {c}.",
-        lambda c: f"satellite imagery of the {c}.",
-        lambda c: f"aerial imagery of the {c}.",
-        lambda c: f"satellite photo of the {c}.",
-        lambda c: f"aerial photo of the {c}.",
-        lambda c: f"satellite view of the {c}.",
-        lambda c: f"aerial view of the {c}.",
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description="RESISC45 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")],
-        )