The dataset viewer is not available for this subset.
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/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/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
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.
CARLA Lane Segmentation Baseline Dataset
Synthetic driving frames collected in the CARLA simulator (v0.9.16) for semantic segmentation of drivable road, lane markings, and background. This is the dataset behind the enhanced baseline in the companion code repository: github.com/sungendary/carla-lane-segmentation
What's here
Each frame is an RGB camera image paired with a semantic-segmentation label (raw class IDs in the R channel, from CARLA's segmentation camera). Data was collected in synchronous mode, so every RGB frame and its label correspond to the same simulation tick, and a speed filter removed near-stationary (red-light) frames to avoid duplicate scenes.
train/ town01.tar.gz ... town07.tar.gz (6 maps, 5 weather sets each)
eval/ town05_eval.tar.gz (held-out map, 3 weather sets)
- Training: 6 maps (Town01, 02, 03, 04, 06, 07) x varied weather (clear, cloudy, wet, rain intensities, sunset) = 30 sets, 1,500 frames each = 45,000 frames.
- Evaluation: Town05 (never used in training) in 3 conditions (ClearNoon, HardRain, ClearSunset), 500 frames each.
Each archive contains rgb/ and label_raw/ folders per set. The
label_vis/ (colorized, human-viewing) masks are not included, since
only label_raw is needed for training.
Class mapping
Labels use CARLA's raw class IDs; the training code remaps them to 3
classes: Road (1) -> 0, RoadLine (24) -> 1, everything else -> 2.
Download and extract
pip install huggingface_hub
# training sets
hf download sungendary/carla-lane-segmentation train/town01.tar.gz --repo-type dataset --local-dir .
# ... repeat for town02..town07 ...
# evaluation set
hf download sungendary/carla-lane-segmentation eval/town05_eval.tar.gz --repo-type dataset --local-dir .
# extract (each archive expands into dataset/townXX_.../)
for f in train/*.tar.gz eval/*.tar.gz; do tar xzf "$f"; done
After extraction you get dataset/town01_clearnoon/rgb, .../label_raw,
etc., ready for the training code.
Collection details
- Simulator: CARLA 0.9.16, synchronous mode, fixed timestep 0.05s (20 FPS)
- Camera: RGB + semantic segmentation, 800x600, 90° FOV, same transform
- Vehicle: autopilot; frames with speed < 1.0 m/s skipped
- See the code repository for the exact collection grid (
configs/sets.yaml) and scripts.
License
MIT. This is synthetic simulator data; no personal or real-world imagery.
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