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pyronear/yolov11s

By pyronear · ↓ 0 · ♥ 0 · Updated 2025-07-22

What's in this repo

This page lists the files in pyronear/yolov11s, including yolov11s.onnx.

  • 1 ONNX models (.onnx files)
  • 8 files, including 2 .gz, 1 .md, 1 .onnx, 1 .pt, 1 .torchscript, 1 .zip
  • Last updated 2025-07-22

ONNX files

File Size Download
yolov11s.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

Pyronear YOLOv11s for Early Wildfire Detection

This repository contains the custom YOLOv11s model trained by Pyronear for early wildfire detection. Our model leverages the powerful capabilities of the Ultralytics YOLOv11 framework to accurately identify potential wildfire hotspots in real-time.

Installation

Install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods including Conda, Docker, and Git, please refer to the Ultralytics Quickstart Guide.

Usage

Python

You can also use the YOLOv11 model in a Python environment:

from ultralytics import YOLO
from PIL import Image

# Load the model
model = YOLO('pyronear/yolov11s.pt')

# Run inference
results = model('path/to/your/image.jpg', conf=0.2, iou=0.1)

# Display results
results.show()

For more examples and detailed usage, see the YOLOv11 Python Docs.

Acknowledgements

This project utilizes the Ultralytics YOLOv11 framework. Special thanks to the Ultralytics team for their support and contributions to the open-source community.

Model info

onnx computer vision wildfire en license:apache-2.0 region:us
↓ 0 ♥ 0

Created: 2025-03-18

Updated: 2025-07-22

View on Hugging Face

Files (8)

.gitattributes
README.md
ncnn_cpu_yolo11s.tar.gz
onnx_cpu_yolo11s.tar.gz
yolov11s.onnx ONNX
yolov11s.pt
yolov11s.torchscript
yolov11s_ncnn_model.zip

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