YOLOv8s object detector for utility poles and towers in street-level imagery. Trained on the Street-level Poles & Towers dataset as part of the HOT-OSM YOLO experiments (curated by UC Berkeley Code For Good cohort).

This is the 50-epoch YOLOv8s model from the original project deliverable. For a lightweight 10-epoch sanity-check baseline, see the related kshitijrajsharma/streetlevel-poles-yolo11n.

Classes

0: pole, 1: tower

Metrics

Best epoch (39 of 49; early-stopped from a 50-epoch schedule, patience 10), validation split, imgsz=640:

metric	value
mAP@50	0.685
mAP@50-95	0.278
precision	0.672
recall	0.686

Note on comparability. These are validation-split metrics logged by Ultralytics during training. The related yolo11n baseline reports test-split metrics (mAP@50 = 0.332), so the two numbers are not directly comparable. A matched test-split evaluation of this model can be added - see results.csv for the full per-epoch training history.

Artifacts: results.png, confusion_matrix.png, confusion_matrix_normalized.png, pr_curve.png, f1_curve.png, val_pred_sample.jpg, results.csv.

Training

Base weights: yolov8s.pt (Ultralytics)
Epochs: 50 configured, stopped at 49 (early stopping, patience 10), best epoch 39
Image size: 640, batch: 16, seed: 42
Full training code: training_notebook.ipynb (original HOT-OSM notebook)

Files

file	description
`best.pt`	best-epoch weights (recommended for inference)
`last.pt`	final-epoch weights
`metrics.json`	training config + best-epoch validation metrics
`data.yaml`	Ultralytics dataset config (class names)
`results.csv`	full per-epoch training/validation metrics
`training_notebook.ipynb`	end-to-end training notebook
`*.png`, `val_pred_sample.jpg`	evaluation plots and a sample prediction

Inference

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="hotosm/streetlevel-poles-yolov8s",
    filename="best.pt",
)
model = YOLO(weights)
results = model("street_image.jpg")
results[0].show()

Related repositories

Dataset: hotosm/streetlevel-poles
Lightweight baseline (10-epoch yolo11n): kshitijrajsharma/streetlevel-poles-yolo11n
Demo Space (runs the yolo11n baseline): kshitijrajsharma/streetlevel-poles-detector

License

Released under CC-BY-SA-4.0, matching the dataset. Training imagery derives from Panoramax / OpenStreetMap-sourced street-level images; share-alike compatibility is preserved.