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YOLOR-comm-mmWave is a fine-tuned object detection model for BS identification for beam initialization to detect
mmWave radio in one inference pass. The model is trained on imagery of Terragraph Sounders from Meta, deployed in indoor commercial spaces. Part of the YOLOR detector family used for the Look Once, Beam Twice mmWave V2X beam-management pipeline (SECON 2026). |
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)
| Architecture | YOLOv11x, 82-class output head (COCO 80 + 2 custom) |
| Initialization | stock yolo11x.pt |
| Schedule | 200 epochs, cos_lr, close_mosaic=20, lr0=0.01 |
| Training data | IndoorCommercialDataset, perceptual-hash deduped (cp_dedup.py, Hamming threshold = 1) — 1,631 train (kept from ~14,386 raw frames) / 1,798 val / 1,799 test |
| Custom classes | radio (id 80), mmWave radio (id 81) |
| Released checkpoint | last.pt |
1from huggingface_hub import hf_hub_download
2from ultralytics import YOLO
3
4weights = hf_hub_download(repo_id="cpnlab/YOLOR-comm-mmWave", filename="last.pt")
5model = YOLO(weights)
6results = model.predict("path/to/image.jpg", conf=0.25)0–79 = COCO; 80 = radio; 81 = mmWave radio.1@inproceedings{biswas2026look,
2 title = {Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional
3 mmWave Beam Management for Vehicular Connectivity},
4 author = {Biswas, Avhishek and Pramanik, Apala and Ekici, Eylem and Vuran, Mehmet C.},
5 booktitle = {Proc. IEEE SECON},
6 year = {2026}
7}