Upload movingthings.py

movingthings.py

@@ -1,12 +1,7 @@
-import os
 import datasets
 from datasets.tasks import ImageClassification
 
-"""MovingThings: The moving things dataset."""
-
-
 _HOMEPAGE = "https://github.com/Rivoks"
-
 _CITATION = """\
 @ONLINE {movingthings,
     author="Rivoks",
@@ -16,25 +11,16 @@ _CITATION = """\
     url="https://github.com/Rivoks"
 }
 """
-
-_DESCRIPTION = """\
-MovingThings is a dataset of images of moving things programmaticaly generated with Stable Diffusion (v1.5).
-It consists of 5 self movement class: roll, flow, zigzag, walk and linear.
-Data was annoted by the script that generates the images with the prompt passed to Stable Diffusion.
-"""
-
-
-_URLS = {
-    "train": "https://huggingface.co/datasets/Rivoks/movingthings/resolve/main/data/train.parquet",
-    "validation": "https://huggingface.co/datasets/Rivoks/movingthings/resolve/main/data/validation.parquet",
-    "test": "https://huggingface.co/datasets/Rivoks/movingthings/resolve/main/data/test.parquet",
-}
-
+_DESCRIPTION = (
+    "MovingThings is a dataset of images of moving things programmaticaly generated "
+    "with Stable Diffusion (v1.5). It consists of 5 self movement classes: roll, flow, zigzag, walk, and linear. "
+    "Data was annotated by the script that generates the images with the prompt passed to Stable Diffusion."
+)
 _NAMES = ["roll", "flow", "zigzag", "walk", "linear"]
 
+class MovingThings(datasets.GeneratorBasedBuilder):
 
-class Beans(datasets.GeneratorBasedBuilder):
-    """Beans plant leaf images dataset."""
+    VERSION = datasets.Version("1.0.0")  # Update the version as needed
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -43,44 +29,13 @@ class Beans(datasets.GeneratorBasedBuilder):
                 {
                     "text": datasets.Value("string"),
                     "image": datasets.Image(),
-                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                    "labels": datasets.ClassLabel(names=_NAMES),
                 }
             ),
             supervised_keys=("image", "labels"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+            task_templates=[
+                ImageClassification(image_column="image", label_column="labels")
+            ],
         )
-
-    def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["train"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["validation"]]),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "files": dl_manager.iter_files([data_files["test"]]),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, files):
-        for i, path in enumerate(files):
-            file_name = os.path.basename(path)
-            if file_name.endswith(".jpg"):
-                yield i, {
-                    "text": path,
-                    "image": path,
-                    "labels": os.path.basename(os.path.dirname(path)).lower(),
-                }