The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
mode: string
model_sha256: string
lidar_used: bool
point_segmentation: string
camera_axes: string
object_axes: string
depth_type: string
image_shape: list<item: int64>
child 0, item: int64
class_names: list<item: string>
child 0, item: string
detection_class_offset: int64
segmentation_class_offset: int64
torch_version: string
id: string
input_sha256: struct<points.npy: string, rgb.png: string, segmentation.png: string, thumbnail.jpg: string, truth.n (... 11 chars omitted)
child 0, points.npy: string
child 1, rgb.png: string
child 2, segmentation.png: string
child 3, thumbnail.jpg: string
child 4, truth.npz: string
inference_seconds: double
satellites: int64
passed: bool
index_sha256: string
satellites_by_split: struct<train: int64, val: int64, test: int64>
child 0, train: int64
child 1, val: int64
child 2, test: int64
examples: int64
to
{'passed': Value('bool'), 'satellites': Value('int64'), 'examples': Value('int64'), 'satellites_by_split': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64')}, 'index_sha256': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in 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 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
mode: string
model_sha256: string
lidar_used: bool
point_segmentation: string
camera_axes: string
object_axes: string
depth_type: string
image_shape: list<item: int64>
child 0, item: int64
class_names: list<item: string>
child 0, item: string
detection_class_offset: int64
segmentation_class_offset: int64
torch_version: string
id: string
input_sha256: struct<points.npy: string, rgb.png: string, segmentation.png: string, thumbnail.jpg: string, truth.n (... 11 chars omitted)
child 0, points.npy: string
child 1, rgb.png: string
child 2, segmentation.png: string
child 3, thumbnail.jpg: string
child 4, truth.npz: string
inference_seconds: double
satellites: int64
passed: bool
index_sha256: string
satellites_by_split: struct<train: int64, val: int64, test: int64>
child 0, train: int64
child 1, val: int64
child 2, test: int64
examples: int64
to
{'passed': Value('bool'), 'satellites': Value('int64'), 'examples': Value('int64'), 'satellites_by_split': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64')}, 'index_sha256': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SpaceSense-Omni examples
680 paired examples covering 136 satellites: 117 ID training satellites, five OOD validation satellites and fourteen OOD test satellites. OOD means absent from gradient training; validation satellites were used to select the checkpoint.
Each satellite has four nearer samples and one farther sample, chosen by fixed 10/20/35/50/90 percentiles of translation distance, without ranking model outputs. Equal-distance ties prefer distinct trajectories, then canonical frame ID. Actual distances, original split, intrinsics, checksums and provenance are in index.json.
Each example includes RGB, original-order sensor FRD points in metres, segmentation, depth/pose/detection ground truth, derived point labels and validity, and cached fusion/RGB predictions. Depth is ray distance, not optical Z. Point labels derive from 2D/depth projection and are not independent manual 3D ground truth. RGB predictions have no point output. No user uploads are included.
The full source training dataset is not distributed here. These examples are a demonstration subset and do not replace the project's full evaluation splits. Publication of these selected assets is authorized by the project owner; no new blanket license for the complete original dataset is asserted by this card.
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