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README.md

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+# YOLOR-radio
+
+![PyTorch](https://img.shields.io/badge/PyTorch-Ultralytics-EE4C2C?logo=pytorch&logoColor=white)
+![YOLOv11](https://img.shields.io/badge/YOLOv11-Detector-00FFFF?logo=yolo&logoColor=black)
+![mmWave](https://img.shields.io/badge/mmWave-V2X-6f42c1)
+![arXiv](https://img.shields.io/badge/arXiv-2605.05071-b31b1b.svg)
+![Venue](https://img.shields.io/badge/IEEE-SECON%202026-00629B)
+
+
+<table>
+<tr>
+<td width="30%" valign="top">
+<img src="radio.png" alt="YOLOR-radio — example radio detection" width="100%">
+</td>
+<td valign="top">
+
+**YOLOR-radio** is a fine-tuned object detection model for BS identification for beam initialization to detect `radio` in one inference pass. The model is trained on imagery of **[Sivers Semiconductors](https://www.sivers-semiconductors.com/) 60 GHz mmWave Radio frontends (EVK06002)**. Part of the YOLOR detector family used for the Look Once, Beam Twice mmWave V2X beam-management pipeline (SECON 2026).
+
+</td>
+</tr>
+</table>
+
+
+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 and Data: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice>
+
+| | |
+|---|---|
+| **Architecture** | YOLOv11x, 81-class output head (COCO 80 + 1 custom) |
+| **Initialization** | stock `yolo11x.pt` |
+| **Schedule** | 200 epochs, `cos_lr`, `close_mosaic=20`, `lr0=0.01` |
+| **Training data** | IndoorCOTSDataset — 3,599 train / 449 val / 451 test |
+| **Custom classes** | `radio` (id 80) |
+| **Released checkpoint** | `last.pt` (the converged final model) |
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+weights = hf_hub_download(repo_id="cpnlab/YOLOR-radio", filename="last.pt")
+model = YOLO(weights)
+
+results = model.predict("path/to/image.jpg", conf=0.25)
+results[0].show()
+```
+
+Class indices in the returned detections: `0–79` are the standard COCO
+classes; `80` is `radio`. The model's `names` dict carries the same
+mapping.
+
+## Intended use
+
+- Stage-1 BS-candidate detector for the Look Once, Beam Twice detector pipeline.
+- General-purpose RF-hardware-aware object detection in indoor / office
+  scenes where both COCO objects and RF radios may appear.
+
+
+## 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> · Code: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice>
+
+## 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.