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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...

TipoOtherVersãoRevision 7b5282c

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Tipo
Other
Tamanho do arquivo
109 MB
Versão
vRevision 7b5282c
Atualizado
16 de set. de 2026
Downloads
116
Tarefas
Detecção de objetos
Fonte
Hugging Face

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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...

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1 fonte
Hugging FacePrimárioVersão 7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e
huggingface.co/cpnlab/YOLOR-Streetlights/tree/7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e

Verificado 16 de set.

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1 artefato

last.pt

pt · 109 MB · SHA-256 398c3a818699…f4e6 · Hugging Face

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--- library_name: ultralytics pipeline_tag: object-detection tags: - yolo - yolov11 - object-detection - coco - 6g - beamforming - vibe - yolor - streetlight --- # YOLOR-Streetlights ![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) ![Streetlight](https://img.shields.io/badge/Urban-Streetlight%20Detection-4c9f38) ![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="streetlight.png" alt="YOLOR-Streetlights — example streetlight detection" width="100%"> </td> <td valign="top"> **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](https://www.unl.edu/) campus**. </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> - 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 ```python 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`...

Source context: 116 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo cpnlab/YOLOR-Streetlights