Duplicate from wesjos/YOLO-head-detection

Co-authored-by: wesjos <wesjos@users.noreply.huggingface.co>

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11m-head.pt

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11n-head.pt

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README.md

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+---
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+- object-detection
+- yolo
+- yolov8
+- yolo11
+- head-detection
+- person-detection
+- ultralytics
+license: agpl-3.0
+---
+
+# YOLO Head Detection
+> Note: This is not a face detection model. Models trained on camera datasets are used for crowd counting, including two angles: one is a top-down view and the other is an oblique view camera.
+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** 
+
+![Six heads, six boxes — 11n-head.pt at conf 0.25](assets/friends_11n.jpg)
+
+Street scene, `11m-head.pt` — four people are visible, and two of the heads are boxed:
+
+![street scene](assets/bus_11m.jpg)
+
+
+### 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
+
+```bash
+pip install ultralytics
+```
+
+```python
+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:
+> ```python
+> 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](https://github.com/ultralytics/ultralytics) — YOLOv8 and YOLO11
+
+## 📄 License
+
+Released under **AGPL-3.0**, inherited from the Ultralytics framework.
+
+## Citation
+
+```bibtex
+@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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