Dataset Viewer

Split (1)

train

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:    ValueError
Message:      Invalid string class label Pillars-Dataset@3145c194d28c2801c28ed70d1b9e9cb334d3e9ed
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 478, 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 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                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 Pillars-Dataset@3145c194d28c2801c28ed70d1b9e9cb334d3e9ed

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Pillars Dataset

This dataset is designed for detecting roadside vertical structures - pillars, poles, and pylons - which are critical static obstacles for autonomous vehicle navigation. It contains 12K bounding box annotations from two side/rear-facing cameras across 6 driving sessions in urban and suburban environments.

Classes (1)

Class	Count	Description
pillar	12,115	Roadside pillar / pole / pylon

Data Fields

Field	Type	Description
bbox_msg_id	VARCHAR	UUID linking to the original bounding box message
object_id	BIGINT	Unique object tracking ID
label	VARCHAR	Object class (`pillar`)
bbox_coords	DOUBLE[4]	Bounding box [x, y, width, height] in pixel coordinates
timestamp_ns	BIGINT	ROS bag timestamp (nanoseconds)
image_path	VARCHAR	Relative path to JPEG in `train/` or `test/`

Data Splits

Split	Rows	Images
train	11,080	1,242
test	1,035	139

Dataset Structure

pillars_dataset/
├── annotations.parquet   # All annotations (12,115 rows)
├── train/
│   ├── camera_1C0FAF5250E2/   # 41 images
│   └── camera_1C0FAF57D6F8/   # 1,201 images
└── test/
    ├── camera_1C0FAF5250E2/   # 40 images
    └── camera_1C0FAF57D6F8/   # 99 images

Usage

import pandas as pd

df = pd.read_parquet("annotations.parquet")
print(f"{len(df)} annotations, {df.image_path.nunique()} unique images")

Downloads last month: 33

Source:

Fleet of autonomous vehicles, urban and suburban roads

Sensor:

2x LUCID TRI054S-CC cameras (2880×1860), front/side facing

Coverage:

~12K bounding box annotations, ~1.4K unique images, single class

Splits:

Train 70% (11,080 rows, 1,242 images), Test 30% (1,035 rows, 139 images) - split by driving session to prevent temporal leakage