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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:    ArrowTypeError
Message:      ("Expected bytes, got a 'list' object", 'Conversion failed for column 7d6fa5c7-a206-4b36-889b-44bed72b0cc8_330 with type object')
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0
              
              During handling of the above exception, another exception occurred:
              
              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 331, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                File "pyarrow/table.pxi", line 4795, in pyarrow.lib.Table.from_pandas
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 640, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                            ~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 628, in convert_column
                  raise e
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 622, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
                  check_status(NdarrayToArrow(pool, values, mask, from_pandas,
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'list' object", 'Conversion failed for column 7d6fa5c7-a206-4b36-889b-44bed72b0cc8_330 with type object')

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V2-SAM Ego2Exo/Exo2Ego FullTrain image closure

Research repack for the V2-SAM Ego2Exo and Exo2Ego training pipelines. It contains the two FullTrain annotations and exactly the JPEG union referenced by them.

This card documents packaging integrity; it does not grant additional rights to the source data. Users remain responsible for complying with the underlying Ego-Exo4D terms and any redistribution requirements.

Why this repack exists

The earlier local Ego-Exo4D-Relation-Train-Mini extraction was not a proven closure of both training directions. Auditing the union of the two FullTrain JSON files found 6,431 referenced paths missing before repair. Training with that snapshot could fail late, silently omit samples in ad-hoc loaders, or make Ego2Exo and Exo2Ego experiments use different image coverage.

This repack fixes the data artifact rather than changing annotations:

  • both official FullTrain JSONs are included unchanged;
  • referenced image paths are de-duplicated across both directions;
  • every referenced JPEG is present in the final image root;
  • every final JPEG passed the image-readability check;
  • files are split into independent standard tar archives;
  • file counts, byte sizes, SHA256 hashes, and repair provenance are retained.

Verified closure

Item Verified value
Ego2Exo records 110,118
Ego2Exo unique referenced images 220,236
Exo2Ego records 123,381
Exo2Ego unique referenced images 246,762
Union referenced JPEGs 249,642
Missing before repair 6,431
Missing after repair 0
Unreadable JPEGs after repair 0
Episode groups 754
Tar shards 4

The two annotation files retain these identities:

File SHA256
Ego2Exo_FullTrain.json bb83cac92179b21833d2465ce29d5b0cf81668d8443d569279313c891472175a
Exo2Ego_FullTrain.json 212e7291990453b087f91dd848dd8a8a8f11a7a0a110fb257be760ca9c8c3058

provenance/final_closure.json is the authoritative closure report. provenance/repair_receipt.json records the repair audit. Its already_present_count=6431 means the final repair rerun found all previously missing paths already restored; it is not a claim that no repair had been needed.

Repository layout

.
├── MANIFEST.json
├── SHA256SUMS
├── README.md
├── data/
│   ├── data_segswap-00000.tar
│   ├── data_segswap-00001.tar
│   ├── data_segswap-00002.tar
│   └── data_segswap-00003.tar
├── json/Ego-Exo4D-Seg/frame-level/
│   ├── Ego2Exo_FullTrain.json
│   └── Exo2Ego_FullTrain.json
└── provenance/
    ├── final_closure.json
    └── repair_receipt.json

Each data/*.tar is independently readable. Extract all four into the same destination so their path trees merge.

Download, verify, and extract

Authenticate with an account that can read this private dataset:

hf download Travor278/V2SAM-EgoExo-Train-Mini-Complete \
  --repo-type dataset \
  --local-dir V2SAM-EgoExo-Train-Mini-Complete

cd V2SAM-EgoExo-Train-Mini-Complete
sha256sum -c SHA256SUMS

mkdir -p extracted
for shard in data/data_segswap-*.tar; do
  tar -xf "$shard" -C extracted
done

Point the V2-SAM dataset sam2_folder at the extracted image tree and select the matching JSON for the desired direction:

  • Ego2Exo: json/Ego-Exo4D-Seg/frame-level/Ego2Exo_FullTrain.json
  • Exo2Ego: json/Ego-Exo4D-Seg/frame-level/Exo2Ego_FullTrain.json

Do not concatenate the JSONs unless the training code explicitly implements a mixed-direction dataset contract.

Integrity files

  • MANIFEST.json: shard counts, sizes, hashes, JSON identities, and closure report identities.
  • SHA256SUMS: command-line integrity verification for every payload and metadata file in this repository.
  • provenance/final_closure.json: before/after missing counts and JPEG audit.
  • provenance/repair_receipt.json: source archive and repair-run evidence.

The repository is complete only when all SHA256 checks pass and the closure report has ready=true, post_repair_missing_count=0, and post_repair_bad_jpeg_count=0.

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