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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...

TypeOtherVersionRevision 7b5282c

Source du modèle

Type
Other
Taille du fichier
109 MB
Version
vRevision 7b5282c
Mis à jour
16 sept. 2026
Téléchargements
116
Tâches
Détection d'objets
Source
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 source
Hugging FacePrincipalVersion 7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e
huggingface.co/cpnlab/YOLOR-Streetlights/tree/7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e

Vérifié 16 sept.

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last.pt

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

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Extraits de sources

2 extraits

--- 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