The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 342, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 345, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
GraspNet-Infinite object splits
Textured, closed, single-object 3D models for tabletop grasping scenes, packed by split.
| zip | objects | what |
|---|---|---|
train_1.zip |
16 | 117 MB |
train_2.zip |
16 | 85 MB |
train_3.zip |
32 | 145 MB |
train_4.zip |
64 | 275 MB |
train_5.zip |
128 | 601 MB |
train_6.zip |
256 | 972 MB |
train_7.zip |
512 | 2105 MB |
train_8.zip |
512 | 2139 MB |
train_9.zip |
512 | 2104 MB |
train_10.zip |
510 | 1612 MB |
train_11.zip |
510 | 1729 MB |
train_12.zip |
509 | 1967 MB |
test_easy.zip |
100 | 296 MB |
test_medium.zip |
100 | 399 MB |
test_hard.zip |
100 | 392 MB |
train_1 to train_12 are disjoint random groups of the training objects. Groups 1 to 9 (16, 16, 32, 64, 128, 256, 512, 512, 512 objects) are drawn from the objects with a real image texture, and groups 1 to 3 passed a visual quality check. Groups 10 to 12 (510, 510 and 509 objects) hold every other training object: image-textured objects not drawn into groups 1 to 9, flat-shaded multi-coloured models, and objects judged textured on sight. test_easy, test_medium and test_hard are the three 100-object test sets, chosen by category and confirmed visually: easy = primitive shapes, medium = geometry composites (hammers, mugs, bowls, toys), hard = very complex geometry (dragons, birds with spread wings, figurines). Training and test objects never share an object.
Each zip holds one folder per object: <uid>/<uid>.glb (Objaverse, textures embedded) or <uid>/model.obj + model.mtl + materials/textures/*.png (Google Scanned Objects), and <uid>/<uid>.json with identity, sub-category, family, assigned longest extent in cm, the scale factor to metres, licence and attribution. manifest.csv at the root lists every object with its group; attribution.csv lists title, author, URL and licence for CC-BY attribution.
Sizes: multiply the stored vertices by scale_to_metres to get the object at its assigned real-world size (assigned_longest_cm).
Sources: Objaverse (CC-BY-4.0 and CC0 objects only) and Google Scanned Objects (CC-BY-4.0).
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