pyronear/yolo11s_sensitive-detector_v1.0.0

Pyronear YOLO model for early wildfire smoke detection.

Release name: Sensitive Detector Version: v1.0.0

Model details

Field	Value
Architecture	yolo11s
Image size	1024
Epochs	50
Optimizer	AdamW
Weights SHA-256	f6f7868833804965...
Training data MD5	fcd56c8728d160e9...

Files

File	Description
best.pt	PyTorch weights
onnx_cpu.tar.gz	ONNX export (cpu)
ncnn_cpu.tar.gz	NCNN export (cpu)
manifest.yaml	Full training manifest

Usage

PyTorch (ultralytics)

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("image.jpg", imgsz=1024, conf=0.2, iou=0.01)
for r in results:
    print(r.boxes)  # bounding boxes + confidences

ONNX (onnxruntime)

from huggingface_hub import hf_hub_download
import onnxruntime as ort
import numpy as np
from PIL import Image

path = hf_hub_download(repo_id="pyronear/yolo11s_sensitive-detector_v1.0.0", filename="onnx_cpu.tar.gz")
session = ort.InferenceSession(path, providers=["CPUExecutionProvider"])

img = Image.open("image.jpg").resize((1024, 1024))
x = np.array(img).transpose(2, 0, 1)[None].astype(np.float32) / 255.0
outputs = session.run(None, {session.get_inputs()[0].name: x})

NCNN

# Unzip first
tar -xzf ncnn_cpu.tar.gz

Download with huggingface_hub

from huggingface_hub import snapshot_download

local_dir = snapshot_download(repo_id="pyronear/yolo11s_sensitive-detector_v1.0.0")

Pyronear engine (sequential smoke detection)

from pyroengine.engine import Engine

engine = Engine(
    conf_thresh=0.20,
    nb_consecutive_frames=5,
)
# feed frames one by one — engine.predict() returns a score
score = engine.predict(pil_image, cam_id="camera_01")
if score > engine.conf_thresh:
    print("Smoke detected!")

About Pyronear

Pyronear builds open-source tools for early wildfire detection.