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cpnlab/YOLOR-comm-mmWave

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YOLOR-comm-mmWave

YOLOR-comm-mmWave is a fine-tuned object detection model for BS identification for beam initialization to detect mmWave radio in one inference pass. The model is trained on imagery of [Terragraph Sounders](https://terragraph.com/) from [Meta](https://about.meta.com/), deployed in indoor commercial spaces. Part of the YOLOR detector family used for the Look Once, Beam Twice mmWave V2X beam-management pipeline (SECON 2026).

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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>

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, 82-class output head (COCO 80 + 2 custom)
Initialization	stock `yolo11x.pt`
Schedule	200 epochs, `cos_lr`, `close_mosaic=20`, `lr0=0.01`
Training data	IndoorCommercialDataset, perceptual-hash deduped (`cp_dedup.py`, Hamming threshold = 1) — 1,631 train (kept from ~14,386 raw frames) / 1,798 val / 1,799 test
Custom classes	`radio` (id 80), `mmWave radio` (id 81)
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-comm-mmWave", filename="last.pt")
model = YOLO(weights)
results = model.predict("path/to/image.jpg", conf=0.25)

Class indices: 0–79 = COCO; 80 = radio; 81 = mmWave radio.

Training data

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
—Apala Pramanik — apramanik2@huskers.unl.edu

Acknowledgments

Developed at the [Cyber Physical Networking (CPN) Lab](https://cpn.unl.edu/), School of Computing, University of Nebraska–Lincoln, in collaboration with The Ohio State University. Thanks to Sivers Semiconductors, Ettus Research, and the open-source Ultralytics, PyTorch, and Ettus UHD communities.