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Realistic-Occlusion-Dataset.py

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+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+from .classes_rod import ROD_CLASSES
+
+
+_CITATION = """\
+@article{BibTeX
+}
+"""
+
+_HOMEPAGE = "https://arielnlee.github.io/PatchMixing/"
+
+_DESCRIPTION = """\
+ROD is meant to serve as a metric for evaluating models' robustness to occlusion. It is the product of a meticulous object collection protocol aimed at collecting and capturing 40+ distinct, real-world objects from 16 classes.
+"""
+
+_DATA_URL = {
+    "rod": [
+        f"https://huggingface.co/datasets/ariellee/Realistic-Occlusion-Dataset/resolve/main/rod_{i}.tar.gz"
+        for i in range(2)
+    ]
+}
+
+
+class ROD(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_WRITER_BATCH_SIZE = 16
+
+    def _info(self):
+        assert len(ROD_CLASSES) == 16
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=list(ROD_CLASSES.values())),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        archives = dl_manager.download(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name="ROD",
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["rod"]],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, archives):
+        """Yields examples."""
+        idx = 0
+        for archive in archives:
+            for path, file in archive:
+                if path.endswith(".jpg"):
+                    synset_id = os.path.basename(os.path.dirname(path))
+                    ex = {"image": {"path": path, "bytes": file.read()}, "label": synset_id}
+                    yield idx, ex
+                    idx += 1

classes_rod.py

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+from collections import OrderedDict
+
+
+ROD_CLASSES = OrderedDict(
+    {
+        1: "banana",
+        2: "baseball",
+        3: "cowboy hat",
+        4: "cup",
+        5: "dumbbell",
+        6: "hammer",
+        7: "laptop",
+        8: "microwave",
+        9: "mouse",
+        10: "orange",
+        11: "pillow",
+        12: "plate",
+        13: "screwdriver",
+        14: "skillet",
+        15: "spatula",
+        16: "vase",
+    }
+)

dataset_info.json

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+{"description": "ROD is meant to serve as a metric for evaluating models' robustness to occlusion. It is the product of a meticulous object collection protocol aimed at collecting and capturing 40+ distinct, real-world objects from 16 classes.\n", "citation": "@article{BibTeX\n}\n", "homepage": "https://arielnlee.github.io/PatchMixing/", "license": "", "features": {"image": {"_type": "Image"}, "label": {"names": ["banana", "baseball", "cowboy hat", "cup", "dumbbell", "hammer", "laptop", "microwave", "mouse", "orange", "pillow", "plate", "screwdriver", "skillet", "spatula", "vase"], "_type": "ClassLabel"}}, "task_templates": [{"task": "image-classification", "label_column": "label"}], "builder_name": "realistic-occlusion-dataset", "config_name": "default", "version": {"version_str": "1.0.0", "major": 1, "minor": 0, "patch": 0}, "splits": {"ROD": {"name": "ROD", "num_bytes": 3306212413, "num_examples": 1231, "shard_lengths": [272, 192, 144, 192, 224, 144, 63], "dataset_name": "realistic-occlusion-dataset"}}, "download_checksums": {"https://huggingface.co/datasets/ariellee/Realistic-Occlusion-Dataset/resolve/main/rod_0.tar.gz": {"num_bytes": 1350865094, "checksum": null}, "https://huggingface.co/datasets/ariellee/Realistic-Occlusion-Dataset/resolve/main/rod_1.tar.gz": {"num_bytes": 1934272362, "checksum": null}}, "download_size": 3285137456, "dataset_size": 3306212413, "size_in_bytes": 6591349869}