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SalahALHaismawi/yolov26-fire-detection

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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YOLOv26 Fire Detection

Real-time fire and smoke detection model based on YOLOv26 (Ultralytics). Achieves 94.9% mAP@50 on fire/smoke detection tasks.

Model Description

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.

Classes

  • —fire - Active flames
  • —smoke - Smoke plumes
  • —other - Related fire indicators

Performance

MetricScore
mAP@5094.9%
mAP@50-9568.0%
Precision89.6%
Recall88.8%

Training Details

ParameterValue
Base ModelYOLOv26-S
Epochs100
Batch Size16
Image Size640x640
OptimizerAdamW
Learning Rate0.01

Usage

Installation

bash
pip install ultralytics

Inference

python
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})")

Video Inference

python
from ultralytics import YOLO

model = YOLO("path/to/best.pt")
results = model.predict("video.mp4", save=True, conf=0.25)

Webcam (Real-time)

python
from ultralytics import YOLO

model = YOLO("path/to/best.pt")
results = model.predict(source=0, show=True, conf=0.25)

Detection Samples

[image]

Training Curves

[image]

Use Cases

  • —Building fire safety monitoring
  • —Wildfire early detection systems
  • —Industrial safety surveillance
  • —Smart home fire detection
  • —Drone-based fire monitoring

Limitations

  • —May have reduced accuracy in low-light conditions
  • —Smoke detection can be affected by fog/steam
  • —Best performance on images similar to training data

Dataset

Trained on fire detection dataset from Roboflow Universe:

  • —8,939 images
  • —License: CC BY 4.0

Citation

bibtex
@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}}
}

License

MIT License

Acknowledgments