refactor: csv name feat: upload script

outdoor_garbage.py

@@ -0,0 +1,73 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {outdoor_garbage},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consisting of garbage cans of various capacities and types.
+Best to train a neural network to monitor the timely removal of garbage and
+organize the logistics of vehicles for garbage collection. Dataset is useful
+for the recommendation systems, optimization and automization the work of 
+community services, smart city.
+"""
+_NAME = 'outdoor_garbage'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class OutdoorGarbage(datasets.GeneratorBasedBuilder):
+    """Small sample of image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'image_id': datasets.Value('int32'),
+                'image': datasets.Image(),
+                'annotations': datasets.Value('string')
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        images = dl_manager.iter_archive(images)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "images": images,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, images, annotations):
+        annotations_df = pd.read_csv(annotations)
+
+        for idx, (image_path, image) in enumerate(images):
+            yield idx, {
+                'image_id':
+                    annotations_df.loc[
+                        annotations_df['image_name'] == image_path]
+                    ['image_id'].values[0],
+                "image": {
+                    "path": image_path,
+                    "bytes": image.read()
+                },
+                'annotations':
+                    annotations_df.loc[
+                        annotations_df['image_name'] == image_path]
+                    ['annotations'].values[0]
+            }