cartoonset

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.formatting.provider": "black"
-}

cartoonset10k.py → cartoonset.py

@@ -25,22 +25,32 @@ The Cartoonset-10k dataset consists of 60000 32x32 colour images in 10 classes,
 per class. There are 50000 training images and 10000 test images.
 """
 
-_DATA_URL = "https://storage.cloud.google.com/cartoonset_public_files/cartoonset10k.tgz"
+_DATA_URLS = {
+    "10k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset10k.tgz",
+    "100k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset100k.tgz",
+}
 
 _NAMES = []
 
 
-class Cartoonset10k(datasets.GeneratorBasedBuilder):
+class Cartoonset(datasets.GeneratorBasedBuilder):
     """Cartoonset-10k Data Set"""
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name="cartoonset10k",
+            name="10k",
             version=datasets.Version("1.0.0", ""),
-            description="Plain text import of Cartoonset-10k Data Set",
-        )
+            description="Loads the Cartoonset-10k Data Set",
+        ),
+        datasets.BuilderConfig(
+            name="100k",
+            version=datasets.Version("1.0.0", ""),
+            description="Loads the Cartoonset-10k Data Set",
+        ),
     ]
 
+    DEFAULT_CONFIG_NAME = "10k"
+
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -58,9 +68,10 @@ class Cartoonset10k(datasets.GeneratorBasedBuilder):
             # ),
         )
 
-    def _split_generators(self, dl_manager):
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        self.config.name
         print("URL:", _DATA_URL)
-        archive = dl_manager.download({"train": _DATA_URL})
+        archive = dl_manager.download(_DATA_URL)
 
         print(archive)
         exit()

dataset_infos.json

@@ -1,39 +0,0 @@
-{
-    "plain_text": {
-        "description": "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images\nper class. There are 20000 training images and 10000 test images.\n",
-        "citation": "@TECHREPORT{Krizhevsky09learningmultiple,\n    author = {Alex Krizhevsky},\n    title = {Learning multiple layers of features from tiny images},\n    institution = {},\n    year = {2009}\n}\n",
-        "homepage": "https://www.cs.toronto.edu/~kriz/cifar.html",
-        "license": "",
-        "features": {
-            "img": {
-                "id": null,
-                "_type": "Image"
-            }
-        },
-        "post_processed": null,
-        "supervised_keys": {
-            "input": "img",
-            "output": "label"
-        },
-        "builder_name": "cifar10",
-        "version": {
-            "version_str": "1.0.0",
-            "description": "",
-            "major": 1,
-            "minor": 0,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 476635078,
-                "num_examples": 20000,
-                "dataset_name": "cifar10"
-            }
-        },
-        "download_size": 476635078,
-        "post_processing_size": null,
-        "dataset_size": 136627438,
-        "size_in_bytes": 307125509
-    }
-}