| --- |
| pipeline_tag: object-detection |
| tags: |
| - fire |
| - smoke |
| - safety |
| - pytorch |
| base_model: |
| - Ultralytics/YOLO26 |
| --- |
| |
| # 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](https://www.kaggle.com/datasets/pengbo00/home-fire-dataset) from Kaggle. |
|
|
|
|
| - **Training Logs:** [ClearML Experiment](https://app.clear.ml/projects/bffe65b5fe1649dd9d202e181ba92fe0/tasks/f57871573c9d4d969dd5867004857d99/scalars) |
|
|
|
|
| ## Evaluation Metrics |
| | Metric | Value | |
| |-----------|-------| |
| | mAP@50 | 0.930 | |
| | mAP@50-95 | 0.626 | |
| | Precision | 0.913 | |
| | Recall | 0.891 | |
|
|
| ## Usage |
| ```python |
| 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 |
