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license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- yolo11
- fire-detection
- computer-vision
- realtime
---
# 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
```python
# train_yolo11n.py
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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 | mAP@0.5 | mAP@0.5:0.95 |
| ----- | ----- | ------- | ------------ |
| 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.
|