The dataset viewer is not available for this split.
Error code: InfoError
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
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 number in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 224, in compute_first_rows_from_streaming_response
info = get_dataset_config_info(path=dataset, config_name=config, token=hf_token)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ProbeScout Main17 task packages
Use with ProbeScout source and setup instructions.
Download only the dataset(s) you need, and extract each ZIP into the code repository root.
The ZIP paths already include dataset/ and visual_analytics/.
| File | Tasks | Compressed size |
|---|---|---|
| cars.zip | 7 | 93.74 MB |
| hico.zip | 8 | 320.29 MB |
| celeba.zip | 2 | 439.43 MB |
Each package includes task definitions, attributes, query image IDs, ordered records, original VQA source/fit/Validation labels, fixed partition contracts, Web score/rank/projection/cluster arrays and the pinned initial F0 evidence. There are no original images, query pictures, thumbnails, historical human feedback, user sessions or human-refined models. Images are supplied locally by the user from the original dataset distributions under their respective terms.
task_packages.json records ZIP and extracted manifest SHA-256 values.
Each extracted dataset/tasks/<dataset>/manifest.json records every payload hash.
The source release pins the immutable HF commit and manifest identities.
Original Val document hashes inside validationProvenance identify the original
training inputs, not the sanitized exported files; the latter have separate hashes.
This preserves compatibility with the published immutable probe banks.
After extracting and placing local images as documented:
uv run --project probe_learning python scripts/prepare_web.py --dataset cars
cd visual_analytics/pcp_analyze/web
npm run dev
Use --dataset hico, --dataset celeba, or multiple dataset names as appropriate.
--without-images prepares numerical views/training but cannot display photographs.
Cached browsing and weight refinement use the task package; training or native
probe updates additionally require features
and, for updates, pretrained banks.
Alternatively extract your own matching SigLIP features using the source scripts.
The original Cars, HICO-DET and CelebA dataset terms still apply.
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