---
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 |
| `v6A_y11n/v6A_y11n_best.pt` | YOLO11n @1280 | 2.59 M | pyro-sdis train + D-Fire train (real only) | v6 edge model (+ ONNX / TFLite fp16 / TFLite INT8 3.6 MB) |

## 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.


## v6 edge release (YOLO11n, real-data-only)

`v6A_y11n/` contains the frozen v6 deliverable: PyTorch checkpoint, ONNX,
TFLite fp16 and TFLite INT8 (3.6 MB), plus `infer.py` — a single-command
offline image/video inference script (PyTorch / ONNX / TFLite backends) —
and the export report.

Frozen pyro-sdis TEST (matched negative-image FP, IoU≥0.4): R@5% 0.243,
R@10% 0.344 — the best nano-scale model of the v6 matrix. Deployment
validation of the INT8 file itself on the project val set: no loss at the
2%/5% FP operating points, −2.1 pt at 10% (traced to graph conversion, not
quantization; fp16 TFLite is bit-identical in accuracy). Suggested
confidence thresholds from the INT8 file's own val curve: 0.73 / 0.68 /
0.62 for ~2 / 5 / 10 % negative-image FP. CPU x86 median forward latency
45.8 ms (proxy; on-device latency unmeasured).

v6 synthetic-data result (pre-registered, reported as-is): neither
procedural compositing nor repaired generative image-to-image synthesis
improved real-data evaluation at matched optimizer steps; seed spread
(±3 pt) exceeded all between-arm differences. Full protocol and reports in
the GitHub repo (`reports/v6/`, `reports/v6/repair/`, `reports/v6/sprint/`).