Saltar al contenido
ModelPublicadoExterno
Descargar

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...

TipoOtherVersiónRevision 7b5282c

Fuente del modelo

Tipo
Other
Tamaño del archivo
109 MB
Versión
vRevision 7b5282c
Actualizado
16 sept 2026
Descargas
116
Tareas
Detección de objetos
Fuente
Hugging Face

Extracto de la fuente

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...

Fuentes

1 fuente
Hugging FacePrincipalVersión 7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e
huggingface.co/cpnlab/YOLOR-Streetlights/tree/7b5282c2509d3cb7909e2fc7cbbfd34ffebc163e

Verificado 16 sept

Artefactos del modelo

1 artefacto

last.pt

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

Descargar

Extractos de fuentes

2 extractos

--- 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