Datasets:

bailuyucha
/

differentiable-render-camouflage-data

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
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: Column() changed from object to string 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 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 304, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

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DRC CARLA Multi-Vehicle Camouflage Dataset

This repository releases the audited synthetic data and geometry assets used to study whether one differentiable vehicle-camouflage generator transfers across vehicle shapes. The release preserves the original split manifests, collection protocols, audit records, and SHA-256 checksums. It is intended for reproducible adversarial-robustness research, including the analysis of negative results.

Release contents

Component	Vehicles and role	Samples	Status
Stage 2 Town10	Audi E-Tron, Tesla Model 3, Ford Mustang, and Jeep Wrangler Rubicon (train); Toyota Prius (validation); Lincoln MKZ 2017 (development holdout)	864	Audited
Stage 3 Town10	Nissan Patrol 2021 (outcome-sealed confirmation)	144	Audited; source-detector confirmation failed
Geometry assets	Attack-UV OBJ, binary attack mask, object-position map, and object-normal map	7 vehicles	Hash-audited
Protocol records	Split manifests, collection plans, preregistrations, audits, and gate decisions	N/A	Versioned JSON

The Stage 2 images are 1152 x 1152 and contain 144 distinct camera positions per vehicle. The Stage 3 set contains the same 144-view grid for Nissan Patrol 2021 under Town10HD_Opt/ClearNoon. Thirty-two clean views used earlier for Nissan mesh alignment are excluded from the released confirmation evaluation.

Raw snapshot layout

Hub path	Files	Exact bytes	Directory-manifest SHA-256
`raw/stage2_town10_v1/`	872	21,787,037,755	`4ac82a74b0c9c8b0918b93b911997b12f6401f3cbd4b0c18914125e715faea67`
`raw/stage3_nissan_patrol_2021_sealed_town10_v1/`	148	3,631,182,409	`89844aa31bd9ca1803b29bb2bddc5379dc8e3ad049c6e4448ce81fbc24dc5c3f`

Each directory has its own SHA256SUMS. The Stage 2 manifest covers 871 payload files (864 samples and seven JSON records); the Stage 3 manifest covers 147 payload files (144 samples and three JSON records). The checksum file itself is excluded from the corresponding manifest and is anchored by the table above.

The immutable raw-data release is tagged raw-v1-20260803. For example, download only the Stage 2 snapshot with the repository utility:

python tools/huggingface_assets.py download /path/to/release \
  --kind dataset \
  --revision raw-v1-20260803 \
  --allow 'raw/stage2_town10_v1/**'

Record the full commit resolved by the tag in experiment provenance rather than relying on the moving main branch.

Sample format

Each sample is a NumPy object array with seven ordered fields:

image
background
mask
discrepancy
labels
camera_position
metadata

Loading requires numpy.load(path, allow_pickle=True) because the record contains heterogeneous arrays and a metadata dictionary. Only load files from a trusted release revision and verify SHA256SUMS first.

Split semantics

Toyota is the validation vehicle used for checkpoint selection.
Lincoln is training-unseen but has been queried during method development, so it is a development holdout rather than a sealed final test.
Nissan is the frozen negative confirmation vehicle and must not be used to tune architectures, objectives, checkpoints, views, or seeds.
A future positive method requires a different, newly reserved confirmation vehicle.

Integrity

The accepted Stage 2 sample aggregate SHA-256 is a1e355159dc4425159d47bc3a88941e60877ca22bd6e8071942873725fd3c04c. Its split manifest SHA-256 is aa358e04bca98ed64dc18a72c41fa9bbcc1af0b1a9cd4483b816dae162275090. The Stage 3 Nissan sample aggregate SHA-256 is 81fa63944b61bc44e28c412331d42507d2db774319db9e1372bd0772f56be3aa; its sealed manifest SHA-256 is 05f30d781d083fc49ea1bbe7dfe861204f28911cecd31ba747e980e07e5db748.

These aggregate values use the project audit procedure recorded in the included provenance files. The two directory-level SHA256SUMS files additionally verify every payload file in the raw snapshots.

Limitations and responsible use

This is synthetic CARLA data from a limited map, weather, detector, and camera protocol. It does not establish physical-world effectiveness or safety. The released checkpoint did not produce a material attack effect on the sealed Nissan vehicle, and no held-out detector evaluation was run after that gate failed. Do not use this dataset to claim broad vehicle, detector, weather, map, or physical transfer without new evidence.

The release is for defensive research, benchmarking, and reproducibility. Users are responsible for complying with applicable laws and deployment policies.

License and attribution

The dataset and project-generated annotations are released under CC BY 4.0. The scenes and vehicle assets are derived from CARLA 0.9.15. CARLA-specific assets are distributed by the CARLA project under CC BY; this release modifies them by exporting geometry, constructing attack UVs, rasterizing masks, and computing object-space position/normal maps. Cite and attribute the CARLA project when using these materials: https://github.com/carla-simulator/carla

The license does not cover unrelated third-party detector or foundation-model weights, which are intentionally excluded.

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Models trained or fine-tuned on bailuyucha/differentiable-render-camouflage-data

bailuyucha/differentiable-render-camouflage-checkpoints

Object Detection • Updated about 23 hours ago