Real-time fire and smoke detection model based on YOLOv26 (Ultralytics). Achieves 94.9% mAP@50 on fire/smoke detection tasks.
This model detects fire, smoke, and related fire indicators in images and videos. Built on YOLOv26-S architecture and trained on 8,939 annotated images.
| Metric | Score |
|---|---|
| mAP@50 | 94.9% |
| mAP@50-95 | 68.0% |
| Precision | 89.6% |
| Recall | 88.8% |
| Parameter | Value |
|---|---|
| Base Model | YOLOv26-S |
| Epochs | 100 |
| Batch Size | 16 |
| Image Size | 640x640 |
| Optimizer | AdamW |
| Learning Rate | 0.01 |
pip install ultralytics
from ultralytics import YOLO
# Load model
model = YOLO("path/to/best.pt")
# Run inference
results = model.predict("image.jpg", conf=0.25)
# Process results
for result in results:
boxes = result.boxes
for box in boxes:
cls = int(box.cls[0])
conf = float(box.conf[0])
label = model.names[cls]
print(f"Detected: {label} ({conf:.2f})")
from ultralytics import YOLO
model = YOLO("path/to/best.pt")
results = model.predict("video.mp4", save=True, conf=0.25)
from ultralytics import YOLO
model = YOLO("path/to/best.pt")
results = model.predict(source=0, show=True, conf=0.25)
Trained on fire detection dataset from Roboflow Universe:
@misc{yolov26-fire-detection,
author = {Salah AL-Haismawi},
title = {YOLOv26 Fire Detection},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/SalahALHaismawi/yolov26-fire-detection}}
}
MIT License