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Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'prediction' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 712, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 757, in _build_writer
self.pa_writer = pq.ParquetWriter(
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
self.writer = _parquet.ParquetWriter(
^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'prediction' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1847, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 731, in finalize
self._build_writer(self.schema)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 757, in _build_writer
self.pa_writer = pq.ParquetWriter(
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
self.writer = _parquet.ParquetWriter(
^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'prediction' with no child field to Parquet. Consider adding a dummy child field.
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 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, 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.
id int64 | annotations list | file_upload string | drafts list | predictions list | data dict | meta dict | created_at timestamp[us, tz=UTC] | updated_at timestamp[us, tz=UTC] | allow_skip bool | inner_id int64 | total_annotations int64 | cancelled_annotations int64 | total_predictions int64 | comment_count int64 | unresolved_comment_count int64 | last_comment_updated_at null | project int64 | updated_by int64 | comment_authors list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
263 | [
{
"bulk_created": false,
"completed_by": 1,
"created_at": "2026-01-03T14:05:30.855478Z",
"draft_created_at": "2026-01-03T14:04:43.739843Z",
"ground_truth": false,
"id": 25,
"import_id": null,
"last_action": null,
"last_created_by": null,
"lead_time": 6075.672999999999,
"p... | 69520220-clip2_caos_READY.mp4 | [] | [] | {
"video": "/data/upload/5/69520220-clip2_caos_READY.mp4"
} | {} | 2026-01-03T14:04:07.504000 | 2026-01-03T15:46:12.204000 | true | 1 | 1 | 0 | 0 | 0 | 0 | null | 5 | 1 | [] |
🚀 compaTAI: Mexico City Chaos Dataset (Video Alpha Sample)"
Training AI to survive where the rules are suggestions.
"compaTAI presents a high-entropy Video Tracking Dataset captured in the chaotic heart of Mexico City (CDMX). While standard datasets (Waymo, nuScenes) are built on predictable environments, compaTAI focuses on "The Chaos Edge Case": the informal and non-linear traffic behaviors unique to Latin American megacities.
🌟 Why Video Tracking?
Unlike static image datasets, our video sequence provides temporal consistency. This allows models to train for Multi-Object Tracking (MOT) and Behavior Prediction, essential for navigating environments where lane markings don't exist and movement is erratic.
📊 Dataset SpecificationsTotal Duration: 52.06 Seconds.
- Total Frames: 1,562 (at 30 FPS).
- Resolution: High Definition (Processed for Fast-Start Streaming).
- Annotation Format: Label Studio JSONL (Temporal Bounding Box Sequences).
- Location 1: Mexico City (Critical Junctions).
- Location 2: State of Mexico (Critical Junctions)
- Type: 2D Video Rectangle Tracking with Interpolated Keyframes.
🏷️ The "Chaos" Taxonomy
Our labels capture the specific "Mexican Edge Cases" that standard sensors often misinterpret:
| Label | Description | Why it matters |
|---|---|---|
| Pedestrian_Irregular | Pedestrians crossing mid-avenue or between cars | Predicts non-linear human trajectory. |
| Street_Vendor | Mobile vendors navigating active traffic lanes. | Unique obstacle detection for informal economies. |
| Infrastructure_Deficit | Missing signals, potholes, or zero lane markings. | Trains defensive driving and path planning. |
| Moto_Filtering | Motorcycles weaving between lanes at high speed. | High-frequency proximity detection. |
| Microbus_Stop | Public transport stopping in the middle of the road. | Predicts sudden traffic flow interruptions. |
🛠️ Data Structure
The .jsonl file contains the temporal sequence of each object. Each annotation includes a sequence array tracking:frame: The exact frame number.x, y, width, height: Normalized coordinates (0-100) for resolution-independent training.time: Exact timestamp within the 52-second clip.
🚀 How to AccessYou can download the raw video and the JSONL metadata directly from this repository to start training your trajectory prediction models.
Python# Coming soon: compaTAI utility script to visualize trackingfrom datasets import load_dataset dataset = load_dataset("manuelvarale/compaTAI-CDMX-Chaos-Alpha")
💰 Commercial Version & Custom ServicesThis repository is a technical sample.
compaTAI offers specialized data for enterprise-grade autonomous systems:Full Video Datasets: 100+ hours of labeled CDMX traffic footage. Specific Edge Cases: Custom recordings of "Rainy Nights" or "Peak Hour at Indios Verdes". Custom Labeling: We use the compaTAI pipeline to label your raw data with our specialized taxonomy. Enterprise Licensing & Custom Data Requests: [manuel.vargas@compatai.mx]
🔗 Visit our Hub: compatai.mx
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