| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - 3d-object-detection |
| - cooperative-perception |
| - autonomous-driving |
| - lidar |
| - camera |
| - v2x |
| --- |
| |
| # CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization |
|
|
| [](https://cisl.ucr.edu/CooperScene) |
| [](https://github.com/UCR-CISL/CooperScene) |
| [](https://huggingface.co/cisl-hf/CooperScene) |
| [](https://pytorch.org/get-started/locally/) |
| [](https://github.com/UCR-CISL/CooperScene/blob/main/LICENSE) |
|
|
| ## Introduction |
|
|
| 🚗 This repository hosts the **model configs and pre-trained checkpoints** for |
| [CooperScene](https://cisl.ucr.edu/CooperScene) — the first real-world, |
| multi-agent, multi-modal cooperative autonomy dataset with C-V2X communication |
| characterization (three connected vehicles + one roadside unit, across |
| intersections, highway ramps, and parking areas). |
|
|
| 🚀 All training and inference code is open-sourced. See the |
| [project page](https://cisl.ucr.edu/CooperScene) and the |
| [GitHub repo](https://github.com/UCR-CISL/CooperScene) for details. |
|
|
| 💬 We welcome feedback and look forward to your comments! |
|
|
| ## What's here |
|
|
| Each model has its config and matching checkpoint together under |
| `configs/<model>/`: |
|
|
| | Cooperative detectors | BEVFusion | |
| |---|---| |
| | `cobevt` | `bevfusion_single_lidar` | |
| | `cosdh` | `bevfusion_single_lidarcam` | |
| | `ermvp` | `bevfusion_coop_lidar` | |
| | `v2vam` | `bevfusion_coop_lidarcam` | |
| | `v2vnet` | | |
| | `v2xvit` | | |
|
|
| All models run on a unified mmengine pipeline (`proj_first=True`, same global-sort |
| BEV/3D polygon-IoU AP @ 0.3 / 0.5 / 0.7), so the numbers are directly comparable. |
|
|
| ## Download |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download cisl-hf/CooperScene --local-dir assets |
| # -> assets/configs/<model>/{<model>.py, <model>.pth} |
| ``` |
|
|
| ## Usage |
|
|
| Clone the [code repo](https://github.com/UCR-CISL/CooperScene), then evaluate or |
| train with a downloaded config + checkpoint: |
|
|
| ```bash |
| # evaluate (test split by default) |
| python tools/test.py assets/configs/ermvp/ermvp.py assets/configs/ermvp/ermvp.pth |
| |
| # train (warm-start from a checkpoint, optional) |
| python tools/train.py assets/configs/ermvp/ermvp.py |
| ``` |
|
|
| See the [GitHub README](https://github.com/UCR-CISL/CooperScene) for data |
| preparation and the Docker workflow. |
|
|
| ## Related links |
|
|
| 🌐 Website: [https://cisl.ucr.edu/CooperScene](https://cisl.ucr.edu/CooperScene) |
|
|
| 💻 GitHub: [https://github.com/UCR-CISL/CooperScene](https://github.com/UCR-CISL/CooperScene) |
|
|
| 🤗 Hugging Face: [https://huggingface.co/cisl-hf/CooperScene](https://huggingface.co/cisl-hf/CooperScene) |
|
|
