Convert dataset to Parquet

README.md

@@ -56,13 +56,20 @@ dataset_info:
           '46': zigzagged
   splits:
   - name: train
-    num_bytes: 448550
+    num_bytes: 463693721.28
     num_examples: 3760
   - name: test
-    num_bytes: 220515
+    num_bytes: 171623828.0
     num_examples: 1880
-  download_size: 625712354
-  dataset_size: 669065
+  download_size: 629499529
+  dataset_size: 635317549.28
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # [DTD: Describable Textures Dataset](https://www.robots.ox.ac.uk/~vgg/data/dtd/)

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:173d40121e4f4ee90e09e5082ab76e124355297b3b72670aec49463115c953bd
-size 177943979
+oid sha256:3e137d5255ddd649a004f19e6b36f170572e2d6504b047f2f4fdb616180fdce3
+size 179155504

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:81bc25a565bf38343376b04f55d1fd81c0e03fb5a2005734d3c06d16ef99ade2
-size 447768375
+oid sha256:a5d4146bc2770ff237b8c5eae693cc44ddfd1162db35de37e9a89b02fd6094eb
+size 450344025

dtd.py

@@ -1,94 +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 GTSRB(ImageFolder):
-    R"""
-    DTD 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"]}),
-        )
-    ]
-
-    classnames = [
-        "banded",
-        "blotchy",
-        "braided",
-        "bubbly",
-        "bumpy",
-        "chequered",
-        "cobwebbed",
-        "cracked",
-        "crosshatched",
-        "crystalline",
-        "dotted",
-        "fibrous",
-        "flecked",
-        "freckled",
-        "frilly",
-        "gauzy",
-        "grid",
-        "grooved",
-        "honeycombed",
-        "interlaced",
-        "knitted",
-        "lacelike",
-        "lined",
-        "marbled",
-        "matted",
-        "meshed",
-        "paisley",
-        "perforated",
-        "pitted",
-        "pleated",
-        "polka-dotted",
-        "porous",
-        "potholed",
-        "scaly",
-        "smeared",
-        "spiralled",
-        "sprinkled",
-        "stained",
-        "stratified",
-        "striped",
-        "studded",
-        "swirly",
-        "veined",
-        "waffled",
-        "woven",
-        "wrinkled",
-        "zigzagged",
-    ]
-
-    clip_templates = [
-        lambda c: f"a photo of a {c} texture.",
-        lambda c: f"a photo of a {c} pattern.",
-        lambda c: f"a photo of a {c} thing.",
-        lambda c: f"a photo of a {c} object.",
-        lambda c: f"a photo of the {c} texture.",
-        lambda c: f"a photo of the {c} pattern.",
-        lambda c: f"a photo of the {c} thing.",
-        lambda c: f"a photo of the {c} object.",
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description="DTD 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")],
-        )