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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': banded
+          '1': blotchy
+          '2': braided
+          '3': bubbly
+          '4': bumpy
+          '5': chequered
+          '6': cobwebbed
+          '7': cracked
+          '8': crosshatched
+          '9': crystalline
+          '10': dotted
+          '11': fibrous
+          '12': flecked
+          '13': freckled
+          '14': frilly
+          '15': gauzy
+          '16': grid
+          '17': grooved
+          '18': honeycombed
+          '19': interlaced
+          '20': knitted
+          '21': lacelike
+          '22': lined
+          '23': marbled
+          '24': matted
+          '25': meshed
+          '26': paisley
+          '27': perforated
+          '28': pitted
+          '29': pleated
+          '30': polka-dotted
+          '31': porous
+          '32': potholed
+          '33': scaly
+          '34': smeared
+          '35': spiralled
+          '36': sprinkled
+          '37': stained
+          '38': stratified
+          '39': striped
+          '40': studded
+          '41': swirly
+          '42': veined
+          '43': waffled
+          '44': woven
+          '45': wrinkled
+          '46': zigzagged
+  splits:
+  - name: train
+    num_bytes: 448550
+    num_examples: 3760
+  - name: test
+    num_bytes: 220515
+    num_examples: 1880
+  download_size: 625712354
+  dataset_size: 669065
+---
+
+# [DTD: Describable Textures Dataset](https://www.robots.ox.ac.uk/~vgg/data/dtd/)
+
+The Describable Textures Dataset (DTD) is an evolving collection of textural images in the wild, annotated with a series of human-centric attributes, inspired by the perceptual properties of textures. 
+This data is made available to the computer vision community for research purposes
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset('tanganke/dtd')
+```
+
+- **Features:**
+  - **Image**: The primary data type, which is a digital image used for classification. The format and dimensions of the images are not specified in this snippet but should be included if available.
+  - **Label**: A categorical feature representing the texture or pattern class of each image. The dataset includes 46 classes with descriptive names ranging from 'banded' to 'zigzagged'.
+    - **Class Labels**:
+      - '0': banded
+      - '1': blotchy
+      - '2': braided
+      - ...
+      - '45': wrinkled
+      - '46': zigzagged
+
+- **Splits**: The dataset is divided into training and test subsets for model evaluation.
+  - **Training**: containing 3760 examples with a total size of 448,550 bytes.
+  - **Test**: containing 1880 examples with a total size of 220,515 bytes.
+

data/test.zip

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+oid sha256:173d40121e4f4ee90e09e5082ab76e124355297b3b72670aec49463115c953bd
+size 177943979

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81bc25a565bf38343376b04f55d1fd81c0e03fb5a2005734d3c06d16ef99ade2
+size 447768375

dtd.py

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+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")],
+        )