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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
file_name: string
video_id: string
title: string
duration_seconds: int64
width: int64
height: int64
fps: int64
file_size_bytes: int64
annotation_type: string
annotation_file: string
separate_audio_file: bool
split: string
sha256: string
source: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1923
to
{'video_id': Value('string'), 'start_time_seconds': Value('int64'), 'end_time_seconds': Value('int64'), 'scene_type': Value('string'), 'time_of_day': Value('string'), 'weather': Value('string'), 'traffic_density': Value('string'), 'road_condition': Value('string'), 'events': Value('string'), 'confidence': Value('string'), 'annotation_notes': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 764, in write_table
self.write_rows_on_file() # in case there are buffered rows to write first
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
file_name: string
video_id: string
title: string
duration_seconds: int64
width: int64
height: int64
fps: int64
file_size_bytes: int64
annotation_type: string
annotation_file: string
separate_audio_file: bool
split: string
sha256: string
source: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1923
to
{'video_id': Value('string'), 'start_time_seconds': Value('int64'), 'end_time_seconds': Value('int64'), 'scene_type': Value('string'), 'time_of_day': Value('string'), 'weather': Value('string'), 'traffic_density': Value('string'), 'road_condition': Value('string'), 'events': Value('string'), 'confidence': Value('string'), 'annotation_notes': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 13 new columns ({'file_name', 'annotation_file', 'separate_audio_file', 'height', 'annotation_type', 'duration_seconds', 'fps', 'sha256', 'width', 'source', 'split', 'title', 'file_size_bytes'}) and 10 missing columns ({'start_time_seconds', 'road_condition', 'weather', 'time_of_day', 'events', 'traffic_density', 'end_time_seconds', 'annotation_notes', 'confidence', 'scene_type'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Chris-davis-thor/transportation/scene_annotations.csv (at revision e2203c88a08899d6f85bfc32986765d32b8f0f68), ['hf://datasets/Chris-davis-thor/transportation@e2203c88a08899d6f85bfc32986765d32b8f0f68/annotations.csv', 'hf://datasets/Chris-davis-thor/transportation@e2203c88a08899d6f85bfc32986765d32b8f0f68/metadata.csv', 'hf://datasets/Chris-davis-thor/transportation@e2203c88a08899d6f85bfc32986765d32b8f0f68/scene_annotations.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
file_name: string
video_id: string
title: string
duration_seconds: int64
width: int64
height: int64
fps: int64
file_size_bytes: int64
annotation_type: string
annotation_file: string
separate_audio_file: bool
split: string
sha256: string
source: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1923
to
{'video_id': Value('string'), 'start_time_seconds': Value('int64'), 'end_time_seconds': Value('int64'), 'scene_type': Value('string'), 'time_of_day': Value('string'), 'weather': Value('string'), 'traffic_density': Value('string'), 'road_condition': Value('string'), 'events': Value('string'), 'confidence': Value('string'), 'annotation_notes': Value('string')}
because column names don't match
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 1694, 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 1880, 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.
video_id string | start_time_seconds int64 | end_time_seconds int64 | scene_type string | time_of_day string | weather string | traffic_density string | road_condition string | events string | confidence string | annotation_notes string |
|---|---|---|---|---|---|---|---|---|---|---|
driver_pov_preview_0001 | 0 | 30 | mountain_road_construction | daytime | clear | medium | dry | road_construction;traffic_cones | medium | Construction barriers and cones are visible near the beginning; the scene transitions toward a scenic stop. |
driver_pov_preview_0001 | 30 | 60 | scenic_viewpoint_and_construction | daytime | clear | medium | dry | parked_vehicles;pedestrians;road_construction | high | Scenic viewpoint and parking area with pedestrians; parked vehicles; barriers; and a mountain background. |
driver_pov_preview_0001 | 60 | 90 | mountain_road_construction | daytime | clear | medium | dry | road_construction;oncoming_vehicles | high | Controlled road section with barriers and oncoming traffic before leaving the work area. |
driver_pov_preview_0001 | 90 | 120 | mountain_road | daytime | clear | low | dry | none | high | Open mountain road with vegetation and distant mountains; little traffic is visible. |
driver_pov_preview_0001 | 120 | 150 | mountain_road | daytime | clear | low | dry | none | high | Open mountain road through a broad valley with no distinct event observed. |
driver_pov_preview_0001 | 150 | 180 | mountain_road | daytime | clear | low | dry | road_sign | medium | Mountain road with a visible roadside sign and valley scenery. |
driver_pov_preview_0001 | 180 | 210 | mountain_road | daytime | hazy | low | dry | none | medium | Mountain road and valley with reduced distant contrast; no distinct traffic event observed. |
driver_pov_preview_0001 | 210 | 240 | mountain_road | daytime | hazy | low | dry | none | high | Mountain road with guard fencing and a distant vehicle; no distinct event observed. |
driver_pov_preview_0001 | 240 | 270 | mountain_road | daytime | hazy | medium | dry | oncoming_vehicles | high | Several oncoming vehicles are visible on the mountain road. |
driver_pov_preview_0001 | 270 | 300 | mountain_road | daytime | clear | low | dry | none | high | Open mountain valley road with low visible traffic. |
driver_pov_preview_0001 | null | null | null | null | null | null | null | null | null | null |
driver_pov_preview_0001 | 0 | 24 | mountain_road_construction | daytime | clear | medium | dry | road_construction;traffic_cones | medium | Approximate boundary: construction barriers and cones dominate the opening section. |
driver_pov_preview_0001 | 24 | 52 | scenic_viewpoint | daytime | clear | medium | dry | parked_vehicles;pedestrians | high | Approximate boundary: viewpoint/parking area with people and parked vehicles is visible. |
driver_pov_preview_0001 | 52 | 78 | mountain_road_construction | daytime | clear | medium | dry | road_construction;oncoming_vehicles | medium | Approximate boundary: controlled road section with barriers and moving traffic. |
driver_pov_preview_0001 | 78 | 150 | mountain_road | daytime | clear | low | dry | none | medium | Approximate boundary: open mountain road and valley scenery with little visible traffic. |
driver_pov_preview_0001 | 150 | 180 | mountain_road | daytime | clear | low | dry | road_sign | medium | Approximate boundary: roadside sign is visible within this section. |
driver_pov_preview_0001 | 180 | 240 | mountain_road | daytime | hazy | low | dry | none | medium | Approximate boundary: reduced distant contrast and open valley road. |
driver_pov_preview_0001 | 240 | 270 | mountain_road | daytime | hazy | medium | dry | oncoming_vehicles | high | Approximate boundary: several oncoming vehicles are visible. |
driver_pov_preview_0001 | 270 | 300 | mountain_road | daytime | clear | low | dry | none | high | Approximate boundary: open mountain valley road with low visible traffic. |
First-Person Mountain Driving Video Sample
Overview
This dataset contains a five-minute first-person driving video recorded on mountain roads. It is provided by ThorData for video understanding, scene analysis, preprocessing, and exploratory computer-vision research.
Files
videos/driver_pov_preview_0001.mp4: source video.metadata.csv: video properties and the file reference used by the dataset viewer.annotations.csv: ten fixed 30-second scene and event intervals.scene_annotations.csv: eight intervals aligned to observed scene changes.ANNOTATION_GUIDE.md: label definitions and annotation rules.
Video Specifications
- Duration: 300 seconds
- Resolution: 720 x 480 pixels
- Frame rate: 20 fps
- File size: 58,419,103 bytes
- Separate audio file: not included
Annotations
The dataset provides two complementary annotation views:
- Fixed-window annotations divide the video into ten consecutive 30-second intervals.
- Scene annotations divide the video at approximate visual scene transitions.
Labels cover scene type, time of day, visible weather, traffic density, road condition, and notable events. Scene-transition boundaries are approximate rather than frame-accurate. No bounding boxes, segmentation masks, tracking IDs, or pixel-level labels are included.
Suggested Uses
- Driving-scene classification
- Temporal scene analysis
- Video decoding and frame extraction
- Video data-loading demonstrations
- Unsupervised and self-supervised representation learning
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