You can deploy this model and start earning money today!

cpnlab/
YOLOR

Name: YOLOR
Rating: 2.5 (13 reviews)

Views

No views yet

YOLOR

PyTorch

YOLOv11

arXiv

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).

YOLOR — example detection of all five custom classes in one inference pass

Source hardware and models

Model	Source hardware / location	Hugging Face
`YOLOR-radio`	Sivers Semiconductors 60 GHz mmWave Radio frontends (EVK06002)	cpnlab/YOLOR-radio
`YOLOR-5GBS`	5G small cells + co-located lamp/utility poles, captured in Downtown Lincoln, Nebraska, USA	cpnlab/YOLOR-5GBS
`YOLOR-comm-mmWave`	Terragraph Sounders from Meta, deployed in indoor commercial spaces	cpnlab/YOLOR-comm-mmWave
`YOLOR-Streetlights`	Urban streetlights on the University of Nebraska–Lincoln campus	cpnlab/YOLOR-Streetlights
`YOLOR` (unified)	Union of all four sources above	this card

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

VIBE five-stage camera-primed beam-management pipeline

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
Apala Pramanik — apramanik2@huskers.unl.edu

Acknowledgments

Developed at the Cyber Physical Networking (CPN) Lab, 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.