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...
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
Fuentes
1 fuenteVerificado 16 sept
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
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      <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
