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--- |
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license: agpl-3.0 |
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library_name: ultralytics |
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pipeline_tag: object-detection |
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tags: |
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- yolo |
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- yolo11 |
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- fire-detection |
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- computer-vision |
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- realtime |
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--- |
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# YOLOv11n Fire Detector (ProFSAM) |
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**Paper:** https://arxiv.org/abs/2510.21782 |
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**Code:** https://github.com/UEmmanuel5/ProFSAM |
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**Weights:** `Fire_best.pt` |
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## Intended use |
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Bounding-box detection of fire to prompt SAM2/MobileSAM/TinySAM in the ProFSAM pipeline. |
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## Training data |
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FASDD subset: classes **fire** and **neither_firenorsmoke** only. Total images used: **51,749** (12,550 fire, 39,199 neither_firenorsmoke). |
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## Training setup (summary) |
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PyTorch 2.0, CUDA 12.4, main GPU: GTX 1050 Ti (4 GB). |
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Ultralytics YOLOv11n initialized then trained 100 epochs. |
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### Script used |
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```python |
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from ultralytics import YOLO |
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import torch |
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torch.backends.cudnn.benchmark = True |
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model = YOLO("path/to/yolo11n.pt") |
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train_results = model.train( |
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data="path/to/FASDD_CV_Fire/data.yaml", |
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epochs=100, |
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imgsz=640, |
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batch=16, |
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optimizer="AdamW", |
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lr0=1e-4, |
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lrf=0.01, |
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dropout=0.15, |
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weight_decay=5e-4, |
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device=0, |
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val=False, |
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save=True, |
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plots=False |
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) |
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```` |
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## Detector metrics (FASDD fire-only subset) |
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| P | R | mAP@0.5 | mAP@0.5:0.95 | |
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| ----- | ----- | ------- | ------------ | |
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| 0.799 | 0.697 | 0.797 | 0.520 | |
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## Test data |
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If you do not have test images, I placed 4 test images from the khan dataset to be used during your testing phase. |
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Test the model [here](https://huggingface.co/spaces/UEmmanuel5/profsam-fire-demo) |
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## ⚖️ License |
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* **Model weights** (`Fire_best.pt`): **AGPL-3.0** (Ultralytics-trained). |
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* **Space code** (this repo): **Apache-2.0**. |
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--- |
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## 🙏 Acknowledgements |
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* Ultralytics YOLO11 |
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* SAM / SAM2 ecosystem and community |
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--- |
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## 📚 Citation |
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If this demo or the model is useful in your research, please cite: |
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**Manuscript** |
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```bibtex |
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@inproceedings{profsam2025, |
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author = {Emmanuel U. Ugwu and Xinming Zhang}, |
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title = {Promptable Fire Segmentation: Unleashing SAM2's Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance}, |
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booktitle = {ICIGP '26}, |
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year = {2026}, |
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address = {Wuhan, China}, |
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month = jan, |
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note = {to appear} |
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} |
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``` |
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**Model/Code** |
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```bibtex |
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@software{profsam2025, |
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author = {Ugwu, Emmanuel U. and Zhang, Xinming}, |
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title = {Promptable Fire Segmentation: Unleashing SAM2’s Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance}, |
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year = {2025}, |
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doi = {10.5281/zenodo.17340313}, |
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url = {https://doi.org/10.5281/zenodo.17340313} |
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} |
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``` |
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