| --- |
| license: other |
| license_name: mixed-source |
| license_link: https://github.com/cair/Fire-Detection-Image-Dataset |
| task_categories: |
| - image-segmentation |
| tags: |
| - fire-detection |
| - instance-segmentation |
| - yolo |
| - ultralytics |
| - safety |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Fire Segmentation Dataset (YOLO-seg format) |
|
|
| Instance-segmentation dataset for **fire** detection: 1,348 images with |
| polygon mask labels in Ultralytics YOLO segmentation format. Built to train |
| [sreeharivp23/fire-segmentation-yolo11n](https://huggingface.co/sreeharivp23/fire-segmentation-yolo11n). |
|
|
| ## Contents |
|
|
| | Split | Images | |
| |---|---| |
| | train | 1,146 | |
| | val | 202 | |
|
|
| - **1,098 fire images** with one or more `fire` polygon instances |
| - **250 negatives** (no fire) with empty label files |
|
|
| ``` |
| fire_seg/ |
| ├── data.yaml # Ultralytics dataset config (1 class: fire) |
| ├── images/{train,val}/ # .jpg / .png images |
| └── labels/{train,val}/ # YOLO-seg .txt labels: "0 x1 y1 x2 y2 ..." (normalised) |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from ultralytics import YOLO |
| |
| path = snapshot_download("sreeharivp23/fire-segmentation-dataset", repo_type="dataset") |
| |
| model = YOLO("yolo11n-seg.pt") |
| model.train(data=f"{path}/data.yaml", epochs=60, imgsz=640) |
| ``` |
|
|
| > Note: `data.yaml` contains a `path:` entry pointing at the original build |
| > location — update it to the downloaded folder (or delete the line and pass |
| > absolute paths) before training. |
|
|
| ## How the labels were made |
|
|
| Labels are **machine-generated**, not human-annotated: |
|
|
| 1. A pretrained YOLOv8 fire/smoke bounding-box detector proposed fire regions. |
| 2. SAM 2.1 (base) converted each box into a pixel mask. |
| 3. Masks were polygonised (OpenCV contours + Douglas-Peucker) into YOLO-seg |
| polygons. |
| 4. Images the detector missed fell back to an HSV flame-colour heuristic |
| (219 images); 12 images yielded nothing and were dropped. |
|
|
| Expect some label noise around smoke/glow boundaries and small flames. A |
| YOLO11n-seg fine-tuned on this data reaches mask mAP50 ≈ 0.54 on the val |
| split. |
|
|
| ## Image sources & licensing |
|
|
| Images originate from two public research datasets; this repackaging adds |
| machine-generated segmentation labels: |
|
|
| - [CAIR Fire-Detection-Image-Dataset](https://github.com/cair/Fire-Detection-Image-Dataset) |
| - [DeepQuestAI Fire-Smoke-Dataset](https://github.com/DeepQuestAI/Fire-Smoke-Dataset) |
|
|
| Check the upstream repositories for their exact terms before commercial use. |
|
|
