YOLO Head Detection

A collection of seven YOLO checkpoints trained to detect human heads in images — useful for crowd counting, privacy blurring, and as a first stage before face recognition.

Trained on a dataset of 15,000 images.

🏆 Recommended: `11n-head.pt`

11n-head.pt is the suggested default for most users

Street scene, 11m-head.pt — four people are visible, and two of the heads are boxed:

Which one should you use?

Default / best overall → 11n-head.pt (see above).
Fastest on a small model → v8n-head.pt (93 FPS, 6.2 MB).
Best balance → v8s-head.pt (90 FPS, 22.5 MB, and zero false boxes on our negative test).
Highest recall → 11m-head.pt / v8m-head.pt (more detections, ~50 FPS).

Note the pattern: every YOLOv8 checkpoint we shipped stays silent on a head-free image, while the YOLO11 family tends to emit low-confidence false boxes. If false positives cost you more than missed heads, start with v8s-head.pt.

🚀 Quick start

pip install ultralytics

from ultralytics import YOLO

# pick any checkpoint from the table above
model = YOLO("v8s-head.pt")

results = model("photo.jpg", conf=0.25, imgsz=640)

for box in results[0].boxes:
    x1, y1, x2, y2 = box.xyxy[0].tolist()
    conf = float(box.conf)
    print(f"head @ ({x1:.0f},{y1:.0f})-({x2:.0f},{y2:.0f})  conf={conf:.2f}")

results[0].save("annotated.jpg")

💡 The checkpoints do not carry class names — class index 0 is the head class. To get readable labels on plotted boxes, map it yourself:
results[0].names = {0: "head"}   # visualisation only

🎓 Training

Dataset size: ~15,000 images
Task: single-class object detection (human heads)
Label convention: class index 0; the checkpoints include no names mapping
Framework: Ultralytics — YOLOv8 and YOLO11

📄 License

Released under AGPL-3.0, inherited from the Ultralytics framework.

Citation

@misc{yolo-head-detection,
  title = {YOLO Head Detection},
  note  = {Seven YOLOv8 / YOLO11 head-detection checkpoints trained on ~15k images},
  year  = {2026}
}

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