Instructions to use mfranzon/fire-smoke-yolov8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use mfranzon/fire-smoke-yolov8 with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("mfranzon/fire-smoke-yolov8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Early Fire and Smoke (YOLOv8)
A YOLOv8n detector finetuned for early domestic fire and smoke: cooking, electrical, and cigarette scenarios. Part of the Model Garden collection of finetuned computer-vision models.
Classes
fire, smoke
Intended use
Designed to pair with a temporal state machine that escalates through clear, smoke present, early warning (growing smoke), fire confirmed, so it alerts on persistent, growing smoke rather than a single noisy frame.
Training
- Base: YOLOv8n
- Data: D-Fire + Domestic Fire and Smoke
- Runtime: ~70 FPS on Apple Silicon (MPS)
Usage
from ultralytics import YOLO
model = YOLO("fire_smoke_yolov8.pt")
results = model("your_video.mp4", stream=True)
for r in results:
r.plot()
Limitations
Vision alone is never a life-safety system. This is a research tool, not a substitute for a certified smoke or fire detector.
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