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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
Town01_prod_Town01_train_000003__present: double
Town01_prod_Town01_train_000006__present: double
Town01_prod_Town01_train_000006__absent: double
Town01_prod_Town01_train_000022__present: double
Town01_prod_Town01_train_000022__absent: double
Town01_prod_Town01_train_000052__present: double
Town01_prod_Town01_train_000052__absent: double
Town01_prod_Town01_train_000107__present: double
Town01_prod_Town01_train_000107__absent: double
Town01_prod_Town01_train_000128__present: double
Town01_prod_Town01_train_000128__absent: double
Town01_prod_Town01_train_000142__present: double
Town01_prod_Town01_train_000142__absent: double
Town01_prod_Town01_train_000158__present: double
Town01_prod_Town01_train_000158__absent: double
Town01_prod_Town01_train_000164__present: double
Town01_prod_Town01_train_000164__absent: double
Town01_prod_Town01_train_000165__present: double
Town01_prod_Town01_train_000165__absent: double
Town01_prod_Town01_train_000168__present: double
Town01_prod_Town01_train_000168__absent: double
Town01_prod_Town01_train_000175__present: double
Town01_prod_Town01_train_000175__absent: double
Town01_prod_Town01_train_000196__present: double
Town01_prod_Town01_train_000196__absent: double
Town01_prod_Town01_train_000197__present: double
Town01_prod_Town01_train_000219__present: double
Town01_prod_Town01_train_000219__absent: double
Town01_prod_Town01_train_000229__present: double
Town01_prod_Town01_train_000236__present: double
Town01_prod_Town01_train_000236__absent: do
...
Town01_prod_Town01_train_000304__present: double
Town01_prod_Town01_train_000304__absent: double
Town01_prod_Town01_train_000309__present: double
Town01_prod_Town01_train_000309__absent: double
split: string
component: string
run_id: string
near_car_5m: bool
frame: string
ego_found: bool
actors: list<item: struct<actor: int64, type: string, box: list<item: double>, range: double, az: double, in (... 316 chars omitted)
  child 0, item: struct<actor: int64, type: string, box: list<item: double>, range: double, az: double, in_fov: bool, (... 304 chars omitted)
      child 0, actor: int64
      child 1, type: string
      child 2, box: list<item: double>
          child 0, item: double
      child 3, range: double
      child 4, az: double
      child 5, in_fov: bool
      child 6, rays_through: int64
      child 7, on_actor: int64
      child 8, on_frac: double
      child 9, blocked_static: int64
      child 10, blocked_veg: int64
      child 11, blocked_dyn: int64
      child 12, indirect: int64
      child 13, indirect_wall: int64
      child 14, indirect_wall_in_domain: int64
      child 15, best_indirect: struct<L: double, relay_range: double, relay_camera_z: double, relay_semantic: int64>
          child 0, L: double
          child 1, relay_range: double
          child 2, relay_camera_z: double
          child 3, relay_semantic: int64
      child 16, cls: string
key: string
route: string
ray_model_residual_m: double
town: string
near_actor_m: double
legacy_metadata: bool
to
{'key': Value('string'), 'split': Value('string'), 'town': Value('string'), 'component': Value('string'), 'route': Value('string'), 'run_id': Value('string'), 'frame': Value('string'), 'ego_found': Value('bool'), 'legacy_metadata': Value('bool'), 'near_actor_m': Value('float64'), 'near_car_5m': Value('bool'), 'ray_model_residual_m': Value('float64'), 'actors': List({'actor': Value('int64'), 'type': Value('string'), 'box': List(Value('float64')), 'range': Value('float64'), 'az': Value('float64'), 'in_fov': Value('bool'), 'rays_through': Value('int64'), 'on_actor': Value('int64'), 'on_frac': Value('float64'), 'blocked_static': Value('int64'), 'blocked_veg': Value('int64'), 'blocked_dyn': Value('int64'), 'indirect': Value('int64'), 'indirect_wall': Value('int64'), 'indirect_wall_in_domain': Value('int64'), 'best_indirect': {'L': Value('float64'), 'relay_range': Value('float64'), 'relay_camera_z': Value('float64'), 'relay_semantic': Value('int64')}, 'cls': Value('string')})}
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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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
              Town01_prod_Town01_train_000003__present: double
              Town01_prod_Town01_train_000006__present: double
              Town01_prod_Town01_train_000006__absent: double
              Town01_prod_Town01_train_000022__present: double
              Town01_prod_Town01_train_000022__absent: double
              Town01_prod_Town01_train_000052__present: double
              Town01_prod_Town01_train_000052__absent: double
              Town01_prod_Town01_train_000107__present: double
              Town01_prod_Town01_train_000107__absent: double
              Town01_prod_Town01_train_000128__present: double
              Town01_prod_Town01_train_000128__absent: double
              Town01_prod_Town01_train_000142__present: double
              Town01_prod_Town01_train_000142__absent: double
              Town01_prod_Town01_train_000158__present: double
              Town01_prod_Town01_train_000158__absent: double
              Town01_prod_Town01_train_000164__present: double
              Town01_prod_Town01_train_000164__absent: double
              Town01_prod_Town01_train_000165__present: double
              Town01_prod_Town01_train_000165__absent: double
              Town01_prod_Town01_train_000168__present: double
              Town01_prod_Town01_train_000168__absent: double
              Town01_prod_Town01_train_000175__present: double
              Town01_prod_Town01_train_000175__absent: double
              Town01_prod_Town01_train_000196__present: double
              Town01_prod_Town01_train_000196__absent: double
              Town01_prod_Town01_train_000197__present: double
              Town01_prod_Town01_train_000219__present: double
              Town01_prod_Town01_train_000219__absent: double
              Town01_prod_Town01_train_000229__present: double
              Town01_prod_Town01_train_000236__present: double
              Town01_prod_Town01_train_000236__absent: do
              ...
              Town01_prod_Town01_train_000304__present: double
              Town01_prod_Town01_train_000304__absent: double
              Town01_prod_Town01_train_000309__present: double
              Town01_prod_Town01_train_000309__absent: double
              split: string
              component: string
              run_id: string
              near_car_5m: bool
              frame: string
              ego_found: bool
              actors: list<item: struct<actor: int64, type: string, box: list<item: double>, range: double, az: double, in (... 316 chars omitted)
                child 0, item: struct<actor: int64, type: string, box: list<item: double>, range: double, az: double, in_fov: bool, (... 304 chars omitted)
                    child 0, actor: int64
                    child 1, type: string
                    child 2, box: list<item: double>
                        child 0, item: double
                    child 3, range: double
                    child 4, az: double
                    child 5, in_fov: bool
                    child 6, rays_through: int64
                    child 7, on_actor: int64
                    child 8, on_frac: double
                    child 9, blocked_static: int64
                    child 10, blocked_veg: int64
                    child 11, blocked_dyn: int64
                    child 12, indirect: int64
                    child 13, indirect_wall: int64
                    child 14, indirect_wall_in_domain: int64
                    child 15, best_indirect: struct<L: double, relay_range: double, relay_camera_z: double, relay_semantic: int64>
                        child 0, L: double
                        child 1, relay_range: double
                        child 2, relay_camera_z: double
                        child 3, relay_semantic: int64
                    child 16, cls: string
              key: string
              route: string
              ray_model_residual_m: double
              town: string
              near_actor_m: double
              legacy_metadata: bool
              to
              {'key': Value('string'), 'split': Value('string'), 'town': Value('string'), 'component': Value('string'), 'route': Value('string'), 'run_id': Value('string'), 'frame': Value('string'), 'ego_found': Value('bool'), 'legacy_metadata': Value('bool'), 'near_actor_m': Value('float64'), 'near_car_5m': Value('bool'), 'ray_model_residual_m': Value('float64'), 'actors': List({'actor': Value('int64'), 'type': Value('string'), 'box': List(Value('float64')), 'range': Value('float64'), 'az': Value('float64'), 'in_fov': Value('bool'), 'rays_through': Value('int64'), 'on_actor': Value('int64'), 'on_frac': Value('float64'), 'blocked_static': Value('int64'), 'blocked_veg': Value('int64'), 'blocked_dyn': Value('int64'), 'indirect': Value('int64'), 'indirect_wall': Value('int64'), 'indirect_wall_in_domain': Value('int64'), 'best_indirect': {'L': Value('float64'), 'relay_range': Value('float64'), 'relay_camera_z': Value('float64'), 'relay_semantic': Value('int64')}, 'cls': Value('string')})}
              because column names don't match

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XField paired data

This repository contains train,validation only. 199 chunks, 3890 complete present/absent frame pairs. Layout: //chunk_NNN/. Root manifest.jsonl lists chunk path, split, town, complete pair/frame counts, and every included file's size and SHA256. Verify all manifest files after download; CHUNK_DONE proves source processing and alone does not prove upload completion.

Town02 is validation. Training towns are under train. Town15 is stored in a separate public repository.

Format and exclusions

Processed chunk bytes are preserved: frames.jsonl, mothers.npy, native.tar, camera.tar, teacher_.npz, census.jsonl, cached rgb arrays/index, obs.json, ego_speed.json, records_clean.jsonl, CHUNK_DONE and RAW_ARCHIVE.json when present. native.tar and camera.tar are required processed shards and are included. raw_frames.tar, nlos_raw_archive and temporary raw capture folders are excluded. Source data was not deleted or rewritten. Local staging uses file links; uploaded content is the actual file bytes.

Absolute paths and download relocation

Source metadata retains absolute paths: frames.jsonl mother (mothers.npy::index) and labels/cam shard references must be rebased after download. Raw provenance fields frame_dir, raw rgb, mother_src and RAW_ARCHIVE.json archive locations describe excluded source archives and may stay unavailable. RQSD uses the included cached rgb arrays/index, mothers.npy, processed shards, teacher labels and census. Provenance paths are not substitutes for processed inputs.

The manifest records absolute_path_markers per file, a byte scan for literal /root/ and /Users/. Compressed payloads are not decoded by that scan. The producer puts keys/arrays in NPZ/NPY and file references in JSON. All original bytes, including two historically rebased migrated chunks, remain unchanged.

This repository is public under user authorization U20. Download it into an absolute DATASET_DIR; authentication is optional. Verify manifest sizes and SHA256 BEFORE relocation. Run the following only on the downloaded copy, never the source:

python3 - "$DATASET_DIR" <<'PY'
from pathlib import Path
import sys,json
root=Path(sys.argv[1]).expanduser().resolve()
for c in root.glob('*/*/chunk_*'):
    if not c.is_dir():continue
    marker='/'+c.parent.name+'/chunks/'+c.name+'/'
    def fix(v):
        if isinstance(v,dict):return {k:fix(x) for k,x in v.items()}
        if isinstance(v,list):return [fix(x) for x in v]
        if isinstance(v,str) and v.startswith('/') and marker in v:
            rel=v.split(marker,1)[1];base,sep,idx=rel.partition('::');dest=c/base
            if dest.is_file():return str(dest)+(sep+idx if sep else '')
        return v
    for f in c.rglob('*'):
        if not f.is_file() or f.suffix not in ('.json','.jsonl') or f.name=='RAW_ARCHIVE.json':continue
        s=f.read_text()
        if f.suffix=='.jsonl':out=''.join(json.dumps(fix(json.loads(l)),ensure_ascii=False)+'\n' for l in s.splitlines() if l)
        else:out=json.dumps(fix(json.loads(s)),ensure_ascii=False,indent=2)+'\n'
        f.write_text(out)
print('Downloaded metadata relocated; original manifest hashes describe the unmodified download.')
PY

The command rebases existing processed files under their recorded capture/chunk prefix and retains ::index and tar offsets. Unavailable raw provenance is left unchanged. Metadata hashes differ after local relocation by design. Set RQSD_NEWDATA to colon-separated downloaded CHUNK_DONE chunk directories; preserve train/validation definitions. Never add the sealed-test repository to that training list.

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