Safety Detection
A fine-tuned YOLO model for detecting fire and smoke in images and video streams, built for real-time safety monitoring.
Model Details
- Architecture: YOLOv26 (fine-tuned)
- Framework: PyTorch
- Epochs: 52
- Experiment Tracking: ClearML
Classes
| ID | Label |
|---|---|
| 0 | fire |
| 1 | smoke |
Dataset
Fine-tuned on the Home Fire Dataset from Kaggle.
- Training Logs: ClearML Experiment
Evaluation Metrics
| Metric | Value |
|---|---|
| mAP@50 | 0.930 |
| mAP@50-95 | 0.626 |
| Precision | 0.913 |
| Recall | 0.891 |
Usage
from ultralytics import YOLO
model = YOLO("path/to/model.pt")
results = model("image.jpg")
Limitations
- Trained on home fire scenarios — performance may degrade in industrial or outdoor environments
- Detection confidence decreases at stricter IoU thresholds (mAP@50-95: 0.626)
- Not validated for production safety-critical systems without further testing
Model tree for e1250/safety_detection
Base model
Ultralytics/YOLO26