TianchenGuan/FireSmockDetect-models

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

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.