---
license: agpl-3.0
pipeline_tag: object-detection
tags:
- yolo
- yolo11
- ultralytics
- wildfire
- smoke-detection
- early-warning
---

# FireSmockDetect — wildfire smoke detectors

Trained checkpoints from the FireSmockDetect project
(code, protocols and full reports: https://github.com/TianchenGuan/FireSmockDetect).
Task: single-class ("smoke") detection on RGB images from fixed lookout
cameras and drones, tuned for FAINT early-stage plumes.

## Checkpoints

| file | arch | params | train data | role |
|---|---|---|---|---|
| `expB_y11s_deployed.pt` | YOLO11s @1280 | 9.4 M | pyro-sdis train + D-Fire train | production default of the project service |
| `v5bA_y11m_best.pt` | YOLO11m @1280 | 20.1 M | same recipe, retrained (v5b arm A, seed 3) | best fixed-camera model on the frozen test |
| `v6_*.pt` (added as they finish) | YOLO11n @1280 | 2.6 M | real ± synthetic (see repo v6 reports) | edge-targeted students |

## Headline numbers (frozen pyro-sdis TEST, matched negative-image FP rate, IoU≥0.4)

| model | R@5%FP | R@10%FP | R@20%FP |
|---|---|---|---|
| v5bA_y11m_best | 0.287 | **0.388** | 0.553 |
| expB_y11s_deployed | 0.258 | 0.354 | 0.501 |

Full per-source tables, aerial-vs-fixed splits, and negative results (data,
distillation and ensemble variants that did NOT beat these) are in the GitHub
reports (`reports/v5b/FINAL_REPORT_V5B.md`).

## Usage

```python
from ultralytics import YOLO
model = YOLO("v5bA_y11m_best.pt")
res = model.predict("image.jpg", imgsz=1280, conf=0.10)
```

Pick the operating confidence from your own tolerated false-positive rate;
the project calibrates thresholds per deployment scenario (fixed camera vs
aerial) — see the repo's eval tooling.

## Limitations

- Faint-smoke recall at low FP is the binding constraint (see table); this is
  an assistance tool, not a certified alarm system.
- Thresholds do NOT transfer between fixed-camera and aerial imagery.
- No early-warning (time-to-first-alert) claim is made: qualified smoke-onset
  footage was unavailable (documented in the v5 protocol).
- Training data: pyro-sdis and D-Fire (original authors' labels). FIgLib /
  HPWREN (CC BY-NC-ND) was never used for training.

## License

AGPL-3.0 (derivatives of Ultralytics YOLO11). Dataset licences remain with
their owners.