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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 2 missing columns ({'position', 'heading'}).
This happened while the json dataset builder was generating data using
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'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/102/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/102/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/102/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/102/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/103/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/103/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/103/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/103/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/104/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/104/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/104/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/104/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/105/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/105/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/105/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/105/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/106/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/106/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/106/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/106/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/109/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/109/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/109/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/109/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/110/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/110/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/110/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/110/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/112/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/112/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/112/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/112/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/113/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/113/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/113/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/113/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/115/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/115/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/115/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/115/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/116/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/116/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/116/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/116/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/117/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/117/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/117/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/117/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/119/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/119/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/119/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/119/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/120/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/120/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/120/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/120/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/122/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/122/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/122/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/122/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/123/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/123/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/123/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/123/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/124/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/124/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/124/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/124/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/139/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/139/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/139/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/139/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/149/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/149/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/149/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/149/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/158/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/158/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/158/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/158/meta/timestamps.json']
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)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: double
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 192
to
{'position': {'x': Value('float64'), 'y': Value('float64'), 'z': Value('float64')}, 'heading': {'w': Value('float64'), 'x': Value('float64'), 'y': Value('float64'), 'z': Value('float64')}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, 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 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 1 new columns ({'text'}) and 2 missing columns ({'position', 'heading'}).
This happened while the json dataset builder was generating data using
hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public/test/seq/001/lidar/timestamps.json (at revision fd53979fb293c17b759c4e7aa67c3780f8b67e4e), ['hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/001/lidar/poses.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/001/lidar/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/001/meta/gps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/001/meta/timestamps.json', 'hf://datasets/Yechi50121/cvpr-workshop-challenge-annoexpert-public@fd53979fb293c17b759c4e7aa67c3780f8b67e4e/test/seq/002/lidar/poses.json', 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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)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.
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End of preview.
🚗 CVPR 2026 Workshop Challenge: Auto-Annotation with Expert-Crafted Guidelines for 3D LiDAR Detection
Welcome to the public dataset repository for the CVPR 2026 Workshop Challenge.
Inspired by recent advancements in foundation models and the critical bottleneck of data annotation in autonomous driving, this challenge introduces a novel paradigm: Auto-Annotation from Expert-Crafted Guidelines.
Instead of training on massive annotated 3D datasets, participants are required to develop models that can comprehend nuanced textual annotator instructions and a few 2D visual examples to predict 3D bounding boxes in LiDAR data. Crucially, no 3D LiDAR visual references are provided in the training phase.
This dataset is built upon the foundational PandaSet dataset, specifically adapted for this multimodal few-shot/zero-shot 3D detection task.
📂 1. Directory Structure
The repository is organized as follows to support the auto-annotation task. It contains the expert guidelines, the 2D few-shot training examples, and the multimodal testing inputs:
cvpr-workshop-challenge-annoexpert-public/
├── annotator_instructions/ # Textual guidelines for each category
│ └── instructions.pdf # Detailed definition & rules per class
├── train/ # Few-Shot 2D Examples (Federated Annotation)
│ ├── images/ # Exemplar images for each category
│ └── labels_2d/ # 2D bounding box annotations
├── test/ # Multimodal Testing Set (Inputs only)
│ ├── images/ # Multi-view test images (200 target frames)
│ └── seq/ # Sequence-based test data
│ ├── {seq_id}/
│ │ ├── lidar/ # LiDAR point clouds
│ │ └── meta/ # Calibration and metadata
├── .gitattributes # Git LFS configuration
└── README.md # Dataset documentation
📄 2. Task Formulation & Data Formats
2.1 Expert Guidelines & Few-Shot Examples (Training)
Participants must rely on the provided guidelines and few-shot examples to understand the 25 target categories.
- Annotator Instructions (
annotator_instructions/): Contains the expert-crafted definitions and rules for annotating each class (e.g., whether to include a rider within a bicycle bounding box). - 2D Visual Examples (
few_shot_examples/):- Naming Convention (
images/):{category_name}&{seq_id}_{camera_name}_{frames_id}.[ext] - Federated Annotation (
labels_2d/): Note that these 2D examples are annotated in a federated way. In a given image, only objects belonging to the target{category_name}are annotated, while objects of other classes are intentionally ignored.- Label Format (
.txt):x y w h cls(wherex,yare the left-top coordinates,w,hare width and height).
- Label Format (
- Naming Convention (
2.2 Test Sensor Data (Evaluation)
The evaluation focuses on 200 specific keyframes.
- Images (
test/images/): Contains 200 multi-view test images. Naming format:{seq_id}_{camera_name}_{frames_id}.[ext]. - LiDAR Point Clouds (
test/seq/): The raw LiDAR sweeps are provided within thetest/seq/{seq_id}/lidar/directory. - Data Access: Although the data is provided here, participants should refer to the official PandaSet Devkit for the standard methods of reading point clouds and handling timestamps. You must match the provided 200 image frames with their corresponding LiDAR sweeps within the
seqfolders using the Devkit's synchronization or indexing logic.
📝 3. Submission Format
For the evaluation server to process your predictions, you must submit your 3D detection results in a highly specific JSON format.
Participants must generate a single submission.json file containing all predictions for the 200 test images. The JSON file should contain a list of dictionaries, where each dictionary represents a single predicted 3D bounding box.
JSON Structure Example:
[
{
"seq_id": "001",
"frame_idx": 29,
"frame_token": "001_front_left_camera_000029",
"label": "Car",
"score": 0.8,
"box_3d": [x, y, z, l, w, h, yaw]
},
{
"seq_id": "001",
"frame_idx": 29,
"frame_token": "001_front_left_camera_000029",
"label": "Pedestrian",
"score": 0.9,
"box_3d": [x, y, z, l, w, h, yaw]
}
]
Field Definitions:
- seq_id (String): The sequence identifier from the PandaSet dataset (e.g., "001").
- frame_idx (Integer): The frame index within the sequence (e.g., 29).
- frame_token (String): The unique identifier for the specific camera frame, formatted as {seq_id}{camera_name}{frame_idx}.
- label (String): The predicted category name. It must exactly match one of the 25 official classes.
- score (Float): The confidence score of the prediction (between 0.0 and 1.0).
- box_3d (List of Floats): The 3D bounding box parameters in the LiDAR coordinate system. It must contain exactly 7 values: [x, y, z, l, w, h, yaw].
- x, y, z: The 3D center coordinates.
- l, w, h: The length, width, and height of the box.
- yaw: The orientation/yaw angle in radians.
Please ensure your final submission is a single valid JSON file named submission.json.
🏷️ 4. Class Categories
The dataset includes 25 distinct object categories, requiring models to handle diverse traffic participants and nuanced object definitions as specified in the Expert Guidelines.
| Vehicle & Transport | Vulnerable Road Users (VRU) | Infrastructure & Obstacles |
|---|---|---|
Car |
Pedestrian |
Temporary_Construction_Barriers |
Pickup_Truck |
Pedestrian_with_Object |
Cones |
Medium-sized_Truck |
Bicycle |
Signs |
Semi-truck |
Motorcycle |
Rolling_Containers |
Bus |
Motorized_Scooter |
Pylons |
Tram_or_Subway |
Personal_Mobility_Device |
Road_Barriers |
Emergency_Vehicle |
Animals-Other |
Construction_Signs |
Other_Vehicle-Construction_Vehicle |
Towed_Object |
|
Other_Vehicle-Uncommon |
||
Other_Vehicle-Pedicab |
Note: Participants must adhere to the specific definitions for each class (e.g., the inclusion of riders in the Bicycle box) as outlined in the provided annotator instructions.
🚀 5. Getting Started
We highly recommend using huggingface-cli or git lfs to download the dataset due to the large size of the high-resolution images.
# Ensure Git LFS is installed
git lfs install
# Clone the dataset repository
git clone [https://huggingface.co/datasets/YOUR_ORG_NAME/cvpr26-auto-annotation-public](https://huggingface.co/datasets/YOUR_ORG_NAME/cvpr26-auto-annotation-public)
# Setup PandaSet Devkit for LiDAR processing
pip install pandaset
Note: Refer to the Official PandaSet Devkit GitHub for instructions on loading LiDAR sweeps based on image frame IDs.
⚖️ 6. License & Citation
This dataset is built upon PandaSet and is subject to the PandaSet License Terms.
If you use this benchmark in your research or challenge submission, please cite our CVPR 2026 Workshop, the AutoExpert baseline paper, and the original PandaSet paper:
@misc{pandaset2021,
title = {PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving},
author = {Xiao, Peng and Shao, Zili and Hao, Shaoyu and others},
booktitle = {IEEE International Intelligent Transportation Systems Conference (ITSC)},
year = {2021},
url = {https://pandaset.org},
}
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