yolov26-fire-detection

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

Training Details

Usage

Installation

pip install ultralytics

Inference

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

from ultralytics import YOLO

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

Webcam (Real-time)

from ultralytics import YOLO

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

Detection Samples

Training Curves

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

@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

• Ultralytics for YOLO implementation
• Roboflow for the dataset