Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
arm_pose: list<item: double>
child 0, item: double
depth_intrinsics: struct<d: list<item: double>, distortion_model: string, frame_id: string, height: int64, k: list<ite (... 48 chars omitted)
child 0, d: list<item: double>
child 0, item: double
child 1, distortion_model: string
child 2, frame_id: string
child 3, height: int64
child 4, k: list<item: double>
child 0, item: double
child 5, p: list<item: double>
child 0, item: double
child 6, width: int64
depth_stamp_ns: int64
depth_units: string
detections: struct<camera_frame: string, detections: list<item: struct<axis_direction_valid: bool, axis_elongati (... 1546 chars omitted)
child 0, camera_frame: string
child 1, detections: list<item: struct<axis_direction_valid: bool, axis_elongation: double, axis_end_width_ratio: double, (... 1316 chars omitted)
child 0, item: struct<axis_direction_valid: bool, axis_elongation: double, axis_end_width_ratio: double, axis_endpo (... 1304 chars omitted)
child 0, axis_direction_valid: bool
child 1, axis_elongation: double
child 2, axis_end_width_ratio: double
child 3, axis_endpoints_base_m: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 4, axis_length_m: double
child 5, axis_mask_pixels: int64
child 6, axis_negative_end_width_px: double
child 7, axis_positive_end_width_px: double
c
...
child 1, reason: null
pick_order: list<item: int64>
child 0, item: int64
labels: list<item: struct<bbox_xywh: list<item: double>, centroid_px: list<item: double>, class_name: string (... 148 chars omitted)
child 0, item: struct<bbox_xywh: list<item: double>, centroid_px: list<item: double>, class_name: string, confidenc (... 136 chars omitted)
child 0, bbox_xywh: list<item: double>
child 0, item: double
child 1, centroid_px: list<item: double>
child 0, item: double
child 2, class_name: string
child 3, confidence: double
child 4, has_mask: bool
child 5, instrument_id: int64
child 6, margin_mm: double
child 7, match_mm: double
child 8, slot: string
child 9, slot_index: int64
child 10, track_id: string
files: struct<depth: string, detections: string, folder: string, image: string, metadata: string, motion: s (... 46 chars omitted)
child 0, depth: string
child 1, detections: string
child 2, folder: string
child 3, image: string
child 4, metadata: string
child 5, motion: string
child 6, videoPrefix: string
child 7, video_dir: string
labelled: bool
topmost_instrument_id: int64
first_pick_candidates: list<item: struct<instrument_id: int64, reason: string>>
child 0, item: struct<instrument_id: int64, reason: string>
child 0, instrument_id: int64
child 1, reason: string
seed: int64
labelling_reason: null
unlabelled_detections: list<item: null>
child 0, item: null
to
{'arrangement': List(Value('int64')), 'arrangement_index': Value('int64'), 'files': {'depth': Value('string'), 'detections': Value('string'), 'folder': Value('string'), 'image': Value('string'), 'metadata': Value('string'), 'motion': Value('string'), 'videoPrefix': Value('string'), 'video_dir': Value('string')}, 'first_pick_candidates': List({'instrument_id': Value('int64'), 'reason': Value('string')}), 'labelled': Value('bool'), 'labelling_reason': Value('null'), 'labels': List({'bbox_xywh': List(Value('float64')), 'centroid_px': List(Value('float64')), 'class_name': Value('string'), 'confidence': Value('float64'), 'has_mask': Value('bool'), 'instrument_id': Value('int64'), 'margin_mm': Value('float64'), 'match_mm': Value('float64'), 'slot': Value('string'), 'slot_index': Value('int64'), 'track_id': Value('string')}), 'overlaps': List(List(Value('int64'))), 'pick_order': List(Value('int64')), 'schema_version': Value('string'), 'seed': Value('int64'), 'set_index': Value('int64'), 'slot_spacing': {'randomised': Value('bool'), 'reason': Value('null')}, 'slots': List({'angle_deg': Value('int64'), 'holding_slot': Value('string'), 'instrument_id': Value('int64'), 'position_mm': {'x_mm': Value('float64'), 'y_mm': Value('float64')}, 'resting_on': List(Value('int64')), 'slot': Value('string'), 'stack_index': Value('int64'), 'supporting': List(Value('int64'))}), 'step_deg': Value('int64'), 'topmost_instrument_id': Value('int64'), 'unit_index': Value('int64'), 'unlabelled_detections': List(Value('null'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
arm_pose: list<item: double>
child 0, item: double
depth_intrinsics: struct<d: list<item: double>, distortion_model: string, frame_id: string, height: int64, k: list<ite (... 48 chars omitted)
child 0, d: list<item: double>
child 0, item: double
child 1, distortion_model: string
child 2, frame_id: string
child 3, height: int64
child 4, k: list<item: double>
child 0, item: double
child 5, p: list<item: double>
child 0, item: double
child 6, width: int64
depth_stamp_ns: int64
depth_units: string
detections: struct<camera_frame: string, detections: list<item: struct<axis_direction_valid: bool, axis_elongati (... 1546 chars omitted)
child 0, camera_frame: string
child 1, detections: list<item: struct<axis_direction_valid: bool, axis_elongation: double, axis_end_width_ratio: double, (... 1316 chars omitted)
child 0, item: struct<axis_direction_valid: bool, axis_elongation: double, axis_end_width_ratio: double, axis_endpo (... 1304 chars omitted)
child 0, axis_direction_valid: bool
child 1, axis_elongation: double
child 2, axis_end_width_ratio: double
child 3, axis_endpoints_base_m: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 4, axis_length_m: double
child 5, axis_mask_pixels: int64
child 6, axis_negative_end_width_px: double
child 7, axis_positive_end_width_px: double
c
...
child 1, reason: null
pick_order: list<item: int64>
child 0, item: int64
labels: list<item: struct<bbox_xywh: list<item: double>, centroid_px: list<item: double>, class_name: string (... 148 chars omitted)
child 0, item: struct<bbox_xywh: list<item: double>, centroid_px: list<item: double>, class_name: string, confidenc (... 136 chars omitted)
child 0, bbox_xywh: list<item: double>
child 0, item: double
child 1, centroid_px: list<item: double>
child 0, item: double
child 2, class_name: string
child 3, confidence: double
child 4, has_mask: bool
child 5, instrument_id: int64
child 6, margin_mm: double
child 7, match_mm: double
child 8, slot: string
child 9, slot_index: int64
child 10, track_id: string
files: struct<depth: string, detections: string, folder: string, image: string, metadata: string, motion: s (... 46 chars omitted)
child 0, depth: string
child 1, detections: string
child 2, folder: string
child 3, image: string
child 4, metadata: string
child 5, motion: string
child 6, videoPrefix: string
child 7, video_dir: string
labelled: bool
topmost_instrument_id: int64
first_pick_candidates: list<item: struct<instrument_id: int64, reason: string>>
child 0, item: struct<instrument_id: int64, reason: string>
child 0, instrument_id: int64
child 1, reason: string
seed: int64
labelling_reason: null
unlabelled_detections: list<item: null>
child 0, item: null
to
{'arrangement': List(Value('int64')), 'arrangement_index': Value('int64'), 'files': {'depth': Value('string'), 'detections': Value('string'), 'folder': Value('string'), 'image': Value('string'), 'metadata': Value('string'), 'motion': Value('string'), 'videoPrefix': Value('string'), 'video_dir': Value('string')}, 'first_pick_candidates': List({'instrument_id': Value('int64'), 'reason': Value('string')}), 'labelled': Value('bool'), 'labelling_reason': Value('null'), 'labels': List({'bbox_xywh': List(Value('float64')), 'centroid_px': List(Value('float64')), 'class_name': Value('string'), 'confidence': Value('float64'), 'has_mask': Value('bool'), 'instrument_id': Value('int64'), 'margin_mm': Value('float64'), 'match_mm': Value('float64'), 'slot': Value('string'), 'slot_index': Value('int64'), 'track_id': Value('string')}), 'overlaps': List(List(Value('int64'))), 'pick_order': List(Value('int64')), 'schema_version': Value('string'), 'seed': Value('int64'), 'set_index': Value('int64'), 'slot_spacing': {'randomised': Value('bool'), 'reason': Value('null')}, 'slots': List({'angle_deg': Value('int64'), 'holding_slot': Value('string'), 'instrument_id': Value('int64'), 'position_mm': {'x_mm': Value('float64'), 'y_mm': Value('float64')}, 'resting_on': List(Value('int64')), 'slot': Value('string'), 'stack_index': Value('int64'), 'supporting': List(Value('int64'))}), 'step_deg': Value('int64'), 'topmost_instrument_id': Value('int64'), 'unit_index': Value('int64'), 'unlabelled_detections': List(Value('null'))}
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.
IRIS VLM Dataset
Data from the IRIS surgical-instrument handling cell. A UFACTORY 850 arm with an Intel RealSense camera picks five surgical instruments from a tray in which they overlap. Each unit is one observation of a randomised tray arrangement, together with the robot motion that followed it.
- 181 units, 46 arrangements, 4 sets per arrangement (
set_index0-3) - 129 units have
labelled: true. The other 52 havelabelled: false, andlabelling_reasonsays why (usually a detection below the 0.50 confidence threshold). - Collected with the dashboard's VLM collection page and finished 2026-10-06.
Instruments
instrument_id |
class_name |
|---|---|
| 1 | scalpel_handle_large |
| 2 | surgical_scissors |
| 3 | scalpel_handle_small |
| 4 | tissue_forceps |
| 5 | artery_forceps |
Layout
VLM_Dataset/
uNNNN_arrA-B-C-D[-E]/
uNNNN_arr....json # observation metadata and labels (iris.vlm.observation.v2)
uNNNN_arr....png # RGB, 1280x720, 8-bit
uNNNN_arr...._depth.png # depth, 1280x720, 16-bit, millimetres, aligned to RGB
uNNNN_arr...._detections.json # raw detector output, arm pose, camera intrinsics (iris.vlm.detections.v1)
uNNNN_motion.json # robot steps for this unit (iris.motion.log.v1)
uNNNN_iris_color_<timestamp>.mp4 # colour-camera video of the motion
u0157 has two videos, because that unit was recorded twice.
Observation metadata (uNNNN_arr....json)
arrangement,arrangement_index,set_index,unit_index,seed,step_deg: which randomised arrangement this unit shows.slots[]: ground-truth placement for each instrument:slot,instrument_id,angle_deg,position_mm,stack_index,resting_on,supporting,holding_slot.overlaps: pairs[a, b]of instrument IDs that overlap.topmost_instrument_id,first_pick_candidates[](withreason, e.g.isolated,on_top),pick_order: the expected answer for which instrument to pick next.labels[]: detections matched to slots:instrument_id,class_name,bbox_xywh(pixels),centroid_px,confidence,has_mask,slot,match_mm,margin_mm.unlabelled_detections[]: detections that could not be matched, with a reason.labelled,labelling_reason: whether every instrument was matched.files: names of the other files in the unit.
Motion log (uNNNN_motion.json)
steps[] lists each robot command in order (command, label, state, ok),
with start and finish times as Unix seconds and as offsets from
recording_started_at. Each step also records joint_positions (6 joints, radians),
tool_pose, and gripper state. Use the offsets to line steps up with the MP4.
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