Datasets:

handsomeYun
/

M2I

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              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 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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.

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M2I Dataset

The M2I dataset is a synthetic dataset introduced in the paper MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection.

M2I is designed to support training and evaluation of models for infrastructure-based multi-camera 3D object detection. It features diverse camera configurations, road layouts, and environmental conditions. This dataset is crucial for developing robust perception systems in intelligent transportation environments and is used in conjunction with frameworks like MIC-BEV.

Paper

MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection

Code

The official code repository for MIC-BEV, which utilizes this dataset, can be found here: https://github.com/HandsomeYun/MIC-BEV

Sample Usage

The M2I dataset is intended to be used with the MIC-BEV codebase for training and evaluation of 3D object detection models. The following snippets demonstrate how to prepare the dataset and perform quick training/evaluation, as outlined in the MIC-BEV GitHub repository.

Prepare Dataset

To prepare the M2I-style data (e.g., V2XSet), use the provided preprocessing script from the MIC-BEV repository:

python MIC-BEV_Official/tools/data_converter/mic-bev/create_v2xset_multiple_map.py

This script processes raw V2XSet-format data and generates the necessary multi-map annotations for training MIC-BEV with M2I.

Quick Start (Training and Evaluation)

After setting up the environment (refer to the GitHub repository's Installation section for full details) and preparing the dataset, you can use the MIC-BEV configuration files for training and evaluation:

Training: Use the MIC-BEV configuration file:

python tools/train.py projects/configs/micbev/mic-bev-seg-gnn.py

Evaluation: Evaluate your trained model:

python tools/test.py projects/configs/micbev/mic-bev-seg-gnn.py /path/to/checkpoint.pth --eval bbox

Citation

If you are using our OpenCDA framework or codes for your development, please cite the following paper:

@misc{zhang2025micbev,
 title        = {MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection},
 author       = {Zhang, Yun and Zheng, Zhaoliang and Liu, Johnson and Huang, Zhiyu and Zhou, Zewei and Meng, Zonglin and Cai, Tianhui and Ma, Jiaqi},
 year         = {2025},
 eprint       = {2510.24688},
 archivePrefix= {arXiv},
 primaryClass = {cs.CV},
 url          = {https://arxiv.org/abs/2510.24688}
}

The arxiv link to the paper: https://arxiv.org/abs/2107.06260

License

This project is released under a UCLA research license.

The MIC-BEV codebase is provided for non-commercial academic research only. Researchers are permitted to use and modify the source code for their own research purposes.

The following uses are not permitted under this license:

Commercial use of any kind
Contracted work that generates corporate or institutional revenue
Redistribution for commercial products or services
Please refer to the LICENSE file for full terms and for information on potential commercial licensing opportunities.

Contributors

MIC-BEV is mainly supported by the UCLA Mobility Lab.

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