Badminton Shuttlecock Detector (YOLOv11s, fine-tuned)

A dedicated YOLOv11s detector for the shuttlecock — a small, fast, low-contrast object that general detectors miss. Trained and run at high resolution (imgsz=1280) to recover per-frame shuttle positions for trajectory building and shot detection.

Task: object detection (task=detect)
Classes: Shuttlecock (1 class)
Base checkpoint: yolo11s.pt (Ultralytics)
Framework: Ultralytics 8.4.87

Intended use

Per-frame shuttle localisation on single-camera broadcast footage. Output positions form the shuttle trajectory that a downstream angle-change detector uses to trigger shot events.

Run at imgsz=1280 — the model was trained at 1280 and small-object recall drops sharply at lower resolutions.

How to use

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

w = hf_hub_download("<your-username>/badminton-shuttlecock-yolov11", "shuttle_yolo11s.pt")
model = YOLO(w)

res = model.predict("frame.jpg", imgsz=1280, conf=0.25)[0]
if len(res.boxes):
    cx, cy = res.boxes.xywh[0][:2].tolist()   # shuttle centre (pixels)

Training


Base model	`yolo11s.pt`
Epochs	100
Image size	1280
Batch	8
Dataset	Roboflow Universe — Shuttlecock (`mathieu-cartron`)
Dataset link	https://universe.roboflow.com/mathieu-cartron/shuttlecock-cqzy3

Evaluation (validation split, from the training checkpoint)

Metric	Value
Precision (box)	0.734
Recall (box)	0.631
mAP@50 (box)	0.709
mAP@50-95 (box)	0.294

Small-object detection is inherently hard; the modest mAP is expected. In practice, running at imgsz=1280 on broadcast frames yields dense-enough per-frame coverage for trajectory reconstruction (e.g. ~211/250 frames on a sampled rally segment in our pipeline). Coverage varies with footage quality and is not a validation metric.

Limitations

Single class; broadcast viewpoint only.
Recall degrades at lower inference resolutions and on motion-blurred / occluded frames.
Small validation split (see source dataset); treat metrics as in-domain.

License

Inherits AGPL-3.0 from Ultralytics YOLO.

Citation

@software{jocher2023yolo,
  author  = {Jocher, Glenn and Qiu, Jing and Chaurasia, Ayush},
  title   = {Ultralytics YOLO},
  url     = {https://github.com/ultralytics/ultralytics},
  version = {11.0.0}, year = {2024}
}
@misc{roboflow_shuttlecock,
  title        = {Shuttlecock Detection Dataset},
  author       = {mathieu-cartron},
  howpublished = {\url{https://universe.roboflow.com/mathieu-cartron/shuttlecock-cqzy3}},
  journal      = {Roboflow Universe}, publisher = {Roboflow}, year = {20XX}
}

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