YOLOR-Streetlights

YOLOR-Streetlights is a fine-tuned object detection model for BS identification for beam initialization to detect urban streetlight infrastructure in one inference pass. Data was collected on the University of Nebraska–Lincoln campus.

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 and Data: 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, 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	Streetlights — 1,498 train / 166 valid / 182 test
Custom classes	streetlight (id 80)
Released checkpoint	last.pt

Usage

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(repo_id="cpnlab/YOLOR-Streetlights", filename="last.pt")
model = YOLO(weights)
results = model.predict("path/to/image.jpg", conf=0.25)

Class indices: 0–79 = COCO; 80 = streetlight.

Training data

Code and Data: https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice

Citation

@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.