Instructions to use TianchenGuan/FireSmockDetect-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use TianchenGuan/FireSmockDetect-models with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("TianchenGuan/FireSmockDetect-models", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from TianchenGuan/FireSmockDetect-models: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
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https://huggingface.co/TianchenGuan/FireSmockDetect-models/resolve/main/README.md
- Command line
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hf download hf://TianchenGuan/FireSmockDetect-models/README.md
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curl -L -o README.md https://huggingface.co/TianchenGuan/FireSmockDetect-models/resolve/main/README.md
2.29 kB
| 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. | |