YOLOv11n Fire Detector (ProFSAM)

Paper: https://arxiv.org/abs/2510.21782
Code: https://github.com/UEmmanuel5/ProFSAM
Weights: Fire_best.pt

Intended use

Bounding-box detection of fire to prompt SAM2/MobileSAM/TinySAM in the ProFSAM pipeline.

Training data

FASDD subset: classes fire and neither_firenorsmoke only. Total images used: 51,749 (12,550 fire, 39,199 neither_firenorsmoke).

Training setup (summary)

PyTorch 2.0, CUDA 12.4, main GPU: GTX 1050 Ti (4 GB).
Ultralytics YOLOv11n initialized then trained 100 epochs.

Script used

from ultralytics import YOLO
import torch
torch.backends.cudnn.benchmark = True

model = YOLO("path/to/yolo11n.pt")
train_results = model.train(
    data="path/to/FASDD_CV_Fire/data.yaml",
    epochs=100,
    imgsz=640,
    batch=16,
    optimizer="AdamW",
    lr0=1e-4,
    lrf=0.01,
    dropout=0.15,
    weight_decay=5e-4,
    device=0,
    val=False,
    save=True,
    plots=False
)

Detector metrics (FASDD fire-only subset)

P	R	[email protected]	[email protected]:0.95
0.799	0.697	0.797	0.520

Test data

If you do not have test images, I placed 4 test images from the khan dataset to be used during your testing phase. Test the model here

⚖️ License

Model weights (Fire_best.pt): AGPL-3.0 (Ultralytics-trained).
Space code (this repo): Apache-2.0.

🙏 Acknowledgements

Ultralytics YOLO11
SAM / SAM2 ecosystem and community

📚 Citation

If this demo or the model is useful in your research, please cite:

Manuscript

@inproceedings{profsam2025,
  author    = {Emmanuel U. Ugwu and Xinming Zhang},
  title     = {Promptable Fire Segmentation: Unleashing SAM2's Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance},
  booktitle = {ICIGP '26},
  year      = {2026},
  address   = {Wuhan, China},
  month     = jan,
  note      = {to appear}
}

Model/Code

@software{profsam2025,
  author    = {Ugwu, Emmanuel U. and Zhang, Xinming},
  title     = {Promptable Fire Segmentation: Unleashing SAM2’s Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance},
  year      = {2025},
  doi       = {10.5281/zenodo.17340313},
  url       = {https://doi.org/10.5281/zenodo.17340313}
}

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