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 Lane-Lines-Dataset@a2524dda9533242756d65ef70e9b1f9226c088a5
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 Lane-Lines-Dataset@a2524dda9533242756d65ef70e9b1f9226c088a5

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Lane Lines Dataset

This dataset is designed for lane line detection in autonomous driving scenarios. It contains polyline annotations representing visible lane boundaries (center lines, edge lines, adjacent lane markings) captured from three vehicle-mounted cameras across driving sessions in urban and suburban environments.

Classes (1)

Class	Count	Description
line	6,376	Lane boundary polyline (5 points = 10 coordinates)

Line IDs

Each annotated line carries an object_id (0–6) indicating a specific lane boundary within the scene:

object_id	Count	Description
0	1,693	Left ego-lane boundary
1	1,686	Right ego-lane boundary
2	1,542	Adjacent lane boundary
3	886	Far lane boundary
4	409	Additional lane marking
5	90	Distant left boundary
6	60	Distant right boundary

Data Fields

Field	Type	Description
msg_id	VARCHAR	UUID linking to the original line message
data	DOUBLE[10]	Polyline as alternating (x, y) pixel coordinates — 5 points
label	VARCHAR	Annotation class (`line`)
object_id	BIGINT	Lane line identifier (0–6)
timestamp_ns	BIGINT	ROS bag timestamp (nanoseconds)
image_path	VARCHAR	Relative path to JPEG in `train/` or `test/`

Data Splits

Split	Rows	Images
train	4,490	1,213
test	1,886	521

Dataset Structure

lines_dataset/
├── annotations.parquet   # All annotations (6,376 rows)
├── train/
│   ├── camera_1C0FAF5250E2/   # 99 images
│   ├── camera_1C0FAF5CA7B6/   # 60 images
│   └── camera_1C0FAF5CC14D/   # 1,054 images
└── test/
    ├── camera_1C0FAF5250E2/   # 51 images
    ├── camera_1C0FAF5CA7B6/   # 30 images
    └── camera_1C0FAF5CC14D/   # 440 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:

3x LUCID TRI054S-CC cameras (2880×1860), front/side facing

Coverage:

~6.4K polyline annotations, ~1.7K unique images, 7 unique lane lines per frame

Splits:

Train 70% (4,490 rows, 1,213 images), Test 30% (1,886 rows, 521 images) - split by image to prevent leakage

Format:

subdirectories

Coordinates:

entry contains alternating (x, y) pairs

Total file size:

1.44 GB