| --- |
| library_name: ultralytics |
| pipeline_tag: object-detection |
| tags: |
| - yolo |
| - yolov11 |
| - object-detection |
| - coco |
| - mmwave |
| - 6g |
| - beamforming |
| - vibe |
| - yolor |
| - unified |
| --- |
| |
| # YOLOR |
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| YOLOR is a fine-tuned object detection model for BS identification for beam initialization to detect **all five YOLOR custom classes** — `radio`, |
| `5G BS`, `LampPost`, `mmWave radio`, `streetlight` — in one inference |
| pass. The combined release |
| model of the YOLOR detector family used for |
| the Look Once, Beam Twice mmWave V2X beam-management pipeline |
| (SECON 2026). |
|
|
| <p align="center"> |
| <img src="all detection.png" alt="YOLOR — example detection of all five custom classes in one inference pass" width="90%"> |
| </p> |
|
|
| ### Source hardware and models |
|
|
| | Model | Source hardware / location | Hugging Face | |
| |---|---|---| |
| | `YOLOR-radio` | [Sivers Semiconductors](https://www.sivers-semiconductors.com/) 60 GHz mmWave Radio frontends (EVK06002) | [cpnlab/YOLOR-radio](https://huggingface.co/cpnlab/YOLOR-radio) | |
| | `YOLOR-5GBS` | 5G small cells + co-located lamp/utility poles, captured in Downtown [Lincoln, Nebraska](https://lincoln.ne.gov/), USA | [cpnlab/YOLOR-5GBS](https://huggingface.co/cpnlab/YOLOR-5GBS) | |
| | `YOLOR-comm-mmWave` | [Terragraph Sounders](https://terragraph.com/) from [Meta](https://about.meta.com/), deployed in indoor commercial spaces | [cpnlab/YOLOR-comm-mmWave](https://huggingface.co/cpnlab/YOLOR-comm-mmWave) | |
| | `YOLOR-Streetlights` | Urban streetlights on the [University of Nebraska–Lincoln](https://www.unl.edu/) campus | [cpnlab/YOLOR-Streetlights](https://huggingface.co/cpnlab/YOLOR-Streetlights) | |
| | `YOLOR` (unified) | Union of all four sources above | this card | |
|
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| Reference implementation for the paper: |
|
|
| > Avhishek Biswas\*, Apala Pramanik\*, Eylem Ekici, Mehmet C. Vuran. |
| > *"Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity."* (\*equal contribution) |
| > |
| > arXiv: <https://doi.org/10.48550/arXiv.2605.05071> |
| |
| <p align="center"> |
| <img src="overview2_updated.png" alt="VIBE five-stage camera-primed beam-management pipeline" width="90%"> |
| </p> |
| |
| |
| |
| ## Quick links |
| |
| - Paper (arXiv): <https://doi.org/10.48550/arXiv.2605.05071> |
| - Code: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice> |
| - Training pipeline: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice/tree/main/YOLOR_Training> |
| |
| | | | |
| |---|---| |
| | **Architecture** | YOLOv11x, **85-class** output head (COCO 80 + 5 custom) | |
| | **Initialization** | stock `yolo11x.pt` | |
| | **Schedule** | 200 epochs, `cos_lr`, `close_mosaic=20`, `lr0=0.01` | |
| | **Training data** | union of all four YOLOR source domains (cots + outdoor + commercial + streetlight), ~10,800 custom train frames + 8,000 COCO replay | |
| | **Custom classes** | `radio` (80), `5G BS` (81), `LampPost` (82), `mmWave radio` (83), `streetlight` (84) | |
| | **Released checkpoint** | `last.pt` | |
| |
| ## Usage |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| from ultralytics import YOLO |
| |
| weights = hf_hub_download(repo_id="cpnlab/YOLOR", filename="last.pt") |
| model = YOLO(weights) |
| results = model.predict("path/to/image.jpg", conf=0.25) |
| ``` |
| |
| Class indices in returned detections: |
| - `0–79` — the 80 standard COCO classes |
| - `80` — `radio` |
| - `81` — `5G BS` |
| - `82` — `LampPost` |
| - `83` — `mmWave radio` |
| - `84` — `streetlight` |
| |
| |
| |
| ## Training data and Code |
| |
| Code and Data: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice> |
| |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{biswas2026look, |
| title = {Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional |
| mmWave Beam Management for Vehicular Connectivity}, |
| author = {Biswas, Avhishek and Pramanik, Apala and Ekici, Eylem and Vuran, Mehmet C.}, |
| booktitle = {Proc. IEEE SECON}, |
| year = {2026} |
| } |
| ``` |
| |
| Paper: <https://doi.org/10.48550/arXiv.2605.05071> |
| |
| ## Contact |
| |
| For questions about this model or the paper, contact the corresponding authors: |
| |
| - **Avhishek Biswas** — [abiswas3@huskers.unl.edu](mailto:abiswas3@huskers.unl.edu) |
| - **Apala Pramanik** — [apramanik2@huskers.unl.edu](mailto:apramanik2@huskers.unl.edu) |
| |
| ## Acknowledgments |
| |
| Developed at the **[Cyber Physical Networking (CPN) Lab](https://cpn.unl.edu/)**, [School of Computing](https://computing.unl.edu/), [University of Nebraska–Lincoln](https://www.unl.edu/), in collaboration with [The Ohio State University](https://www.osu.edu/). Thanks to [Sivers Semiconductors](https://www.sivers-semiconductors.com/), [Ettus Research](https://www.ettus.com/), and the open-source [Ultralytics](https://ultralytics.com/), [PyTorch](https://pytorch.org/), and [Ettus UHD](https://www.ettus.com/) communities. |
| |
