NICO++: Towards Better Benchmarking for Domain Generalization
Paper • 2204.08040 • Published
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The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Invalid string class label NICOplusplus@66bea9905018646633b3c3664027e7e80cdcb687
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1537, in _prepare_split_single
example = self.info.features.encode_example(record) if self.info.features is not None else record
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label NICOplusplus@66bea9905018646633b3c3664027e7e80cdcb687
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1382, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1560, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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End of preview.
NICO++ DG Benchmark Subset (Unofficial)
Dataset Summary
This is a non-official subset of the NICO++ dataset, designed for Domain Generalization (DG) evaluation. We select 20 categories across 6 domains.
The dataset can be used to benchmark algorithms for domain generalization, domain adaptation, and robustness testing.
⚠️ Note: This dataset is not the official release of NICO++, but a re-organized subset curated for research purposes.
Supported Tasks and Leaderboards
- Domain Generalization (DG)
- Out-of-Distribution (OOD) Robustness
- Representation Learning with Multiple Contexts
Languages
- Images contain natural objects and scenes; no text annotations.
- Labels are in English.
Dataset Structure
Data Fields
Each sample contains:
image: the input image (RGB)label: the class label (integer)category: the semantic category (string, one of 20)domain: the environment/domain (string, one of 6)
Domains
We follow the DG benchmark setup:
"domains" = ['autumn', 'dim', 'grass', 'outdoor', 'rock', 'water']
Categories
The selected 20 categories are:
"categories" = [
'kangaroo', 'dolphin', 'sailboat', 'pumpkin','gun','sheep','tent','mailbox','cactus','car',
'spider','tortoise','fox','lion','elephant','racket','umbrella','crab','giraffe','chair'
]
Data Splits
The dataset is split into domains rather than standard train/val/test.
- Researchers may adopt leave-one-domain-out DG evaluation, where training uses 5 domains and testing uses the held-out one.
- Example: Train on {autumn, dim, grass, outdoor, rock}, Test on {water}.
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