Fire Smoke Yolov8n

Files

• best.pt — fine-tuned weights (use this)
• last.pt — final-epoch weights

Results (test split, 4,306 images)

Training config

• Base: yolov8n.pt (COCO pretrained)
• Epochs: 50, image size: 640, batch: 16
• Optimizer: MuSGD (auto), lr0=0.01
• Device: Apple MPS

Usage

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

ckpt = hf_hub_download(repo_id="rabahdev/fire-smoke-yolov8n", filename="best.pt")
model = YOLO(ckpt)
results = model("image.jpg")
results[0].show()

Dataset

• D-Fire (smoke + fire detection), YOLO format
• Train: 14,122 — Val: 3,099 — Test: 4,306

License

AGPL-3.0 (inherited from Ultralytics YOLOv8).

--- license: agpl-3.0 tags: - ultralytics - yolov8 - object-detection - fire-detection - smoke-detection - pytorch library_name: ultralytics pipeline_tag: object-detection --- # YOLOv8n — Fire & Smoke Detector (D-Fire fine-tune) YOLOv8n fine-tuned on the **D-Fire** dataset for detecting **smoke** and **fire** in images. ## Classes - `0` — smoke - `1` — fire ## Files - `best.pt` — fine-tuned weights (use this) - `last.pt` — final-epoch weights ## Results (test split, 4,306 images) | Metric | All | Smoke | Fire | |---|---|---|---| | mAP50 | 0.754 | — | — | | mAP50-95 | 0.430 | 0.499 | 0.362 | | Precision | 0.766 | — | — | | Recall | 0.688 | — | — | ## Training config - Base: `yolov8n.pt` (COCO pretrained) - Epochs: 50, image size: 640, batch: 16 - Optimizer: MuSGD (auto), lr0=0.01 - Device: Apple MPS ## Usage ```python from huggingface_hub import hf_hub_download from ultralytics import YOLO ckpt = hf_hub_download(repo_id="rabahdev/fire-smoke-yolov8n", filename="best.pt") model = YOLO(ckpt) results = model("image.jpg") results[0].show() ``` ## Dataset - D-Fire (smoke + fire detection), YOLO format - Train: 14,122 — Val: 3,099 — Test: 4,306 ## License AGPL-3.0 (inherited from Ultralytics YOLOv8).