Polylane Roadway Yolo11l Seg

--- license: apache-2.0 library_name: ultralytics tags: - object-detection - instance-segmentation - yolo - yolo11 - ultralytics - aerial - drone - road-segmentation - roadway pipeline_tag: image-segmentation --- # polylane-roadway-yolo11l-seg YOLO11-l-seg fine-tuned for **roadway (asphalt road surface) segmentation** in aerial drone footage of urban / residential streets. ## Training - **Base model**: `yolo11l-seg.pt` (Ultralytics, COCO-pretrained) - **Data**: ~2 973 frames (2 646 train / 327 val) automatically annotated with **SAM 3** text-prompted segmentation across 6 drone videos (~50 K frames total), Kashkadarya region (Uzbekistan) - **Split**: train = 5 videos, val = 1 video (no overlap) - **Image size**: 1280 - **Batch size**: 16 × 3 GPUs (NVIDIA B200) - **Epochs**: 50, AdamW, cos LR, lr0=0.001, mixup=0.1, copy_paste=0.1 - **Wall time**: ~28 min on 3× B200 ## Metrics (val on held-out video) | Metric | Best epoch (38) | Last epoch | |---|---|---| | Box mAP50 | 0.73 | 0.73 | | Box mAP50-95 | 0.44 | 0.42 | | **Mask mAP50** | **0.604** | 0.58 | | **Mask mAP50-95** | **0.415** | 0.40 | The roadway is a "stuff" class (one large region per image), so instance mAP under-reports semantic-segmentation quality. Visual mask IoU is closer to **0.70–0.80** on most frames. ## Limitations - Trained on residential / village aerial drone footage only — generalisation to highway/dashcam not validated. - Training labels come from SAM 3 auto-annotation, which fails (~5–15 %) on far frames where grey rooftops are confused with asphalt. Some noise inherited. ## Usage ```python from ultralytics import YOLO model = YOLO("best.pt") results = model.predict("frame.jpg", imgsz=1280, conf=0.25) results[0].show() ``` ## License Apache 2.0

Source context: 49 downloads · 0 likes · Pipeline image-segmentation · Library ultralytics · Repo dastrix/polylane-roadway-yolo11l-seg