Yolo11s Sensitive Detector

--- datasets: - pyronear/pyro-dataset license: apache-2.0 tags: - wildfire - fire-detection - yolo - object-detection - pyronear --- # pyronear/yolo11s_sensitive-detector Pyronear YOLO model for early wildfire smoke detection. **Release name:** Sensitive Detector **Latest version:** v1.1.0 Each release is a git tag on this repo (e.g. `v1.1.0`). Pin a version with `revision="v1.1.0"` in `hf_hub_download` / `snapshot_download`. ## Model details | Field | Value | |---|---| | Architecture | yolo11s | | Image size | 1024 | | Epochs | 50 | | Optimizer | AdamW | | Weights SHA-256 | `a9bfa11c559e4b22...` | | Training data MD5 | `409302377938ce2a...` | ## 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) ```python 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) ```python 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", 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 ```bash # Unzip first tar -xzf ncnn_cpu.tar.gz ``` ### Download with huggingface_hub ```python from huggingface_hub import snapshot_download local_dir = snapshot_download(repo_id="pyronear/yolo11s_sensitive-detector") # latest local_dir = snapshot_download(repo_id="pyronear/yolo11s_sensitive-detector", revision="v1.1.0") # pinned ``` ### Pyronear engine (sequential smoke detection) ```python 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](https://pyronear.org) builds open-source tools for early wildfire de...

Source context: 0 downloads · 2 likes · Pipeline object-detection · Repo pyronear/yolo11s_sensitive-detector

Source context: 0 downloads · 1 likes · Pipeline object-detection · Repo pyronear/yolo11s_sensitive-detector