Instructions to use chanubc/overhead-person-yolo11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use chanubc/overhead-person-yolo11 with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("chanubc/overhead-person-yolo11", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
overhead-person-yolo11s
YOLO11s fine-tuned for overhead / ceiling-camera person detection. Part of an on-device (browser WebGPU) cooking-robot safety system.
- Base:
yolo11s.pt(COCO-pretrained), full fine-tuning - Data: overhead-person (Roboflow Universe, CC BY 4.0), re-split 80/10/10
- Single class:
person
Results (test split)
| metric | stock yolo11s (before) | fine-tuned (after) |
|---|---|---|
| recall | 0.442 | 0.980 |
| precision | 0.627 | 0.969 |
| mAP@50 | 0.506 | 0.991 |
| mAP@50-95 | 0.160 | 0.752 |
Files
best.ptโ PyTorch weights (ultralytics)best.onnxโ ONNX opset 12, for onnxruntime-web / WebGPU on-device inference
Usage
from ultralytics import YOLO
YOLO("best.pt").predict("image.jpg", conf=0.25)
Code & write-up: https://github.com/chanubc/overhead-person-yolo11
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