Streetlevel Poles Yolo11n

--- license: cc-by-sa-4.0 library_name: ultralytics pipeline_tag: object-detection tags: - yolo - yolov11 - ultralytics - object-detection - poles - towers - streetlevel datasets: - kshitijrajsharma/streetlevel-poles base_model: Ultralytics/YOLOv11 --- # streetlevel-poles-yolo11n YOLOv11n object detector for utility **poles** and **towers** in street-level imagery. Trained on the [Street-level Poles & Towers](https://huggingface.co/datasets/kshitijrajsharma/streetlevel-poles) dataset (curated by UC Berkeley, HOT-OSM YOLO experiments). ## Classes `0: pole`, `1: tower` ## Metrics (test split) | metric | value | |-----------|------:| | mAP@50 | 0.332 | | mAP@50-95 | 0.120 | | precision | 0.455 | | recall | 0.345 | 10 epochs, `imgsz=640`. This is a sanity-check baseline, not a tuned model. ## Try it in your browser Interactive Gradio demo: <https://huggingface.co/spaces/kshitijrajsharma/streetlevel-poles-detector> ## Inference (local) ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO weights = hf_hub_download( repo_id="kshitijrajsharma/streetlevel-poles-yolo11n", filename="best.pt", ) model = YOLO(weights) results = model("street_image.jpg") results[0].show() ``` ## Files - `best.pt` — trained weights (Ultralytics format, ~5 MB). - `results.png`, `confusion_matrix.png`, `pr_curve.png` — training diagnostics. - `metrics.json` — machine-readable metrics summary. ## License CC-BY-SA-4.0.