elvinguseinov/wildfire-detection-yolo11-yolo26-nano

Wildfire Detection: YOLOv11-Nano & YOLOv26-Nano

This repository contains the PyTorch weights (.pt) for YOLOv11-Nano and YOLOv26-Nano models, which are optimized for real-time edge-based wildfire and smoke detection. These models were trained and benchmarked to balance strict hardware constraints (limited memory, low computational power, and energy budgets) with high detection accuracy.

Model Details

• —Architecture: YOLOv11-Nano and YOLOv26-Nano
• —Task: Object Detection (Bounding Box)
• —Classes: Smoke, Fire
• —Target Deployment: Autonomous edge devices like Unmanned Aerial Vehicles (UAVs) and embedded systems (e.g., Raspberry Pi).

Performance Metrics

The models were rigorously evaluated on the Smoke-Fire Dataset. Below are the baseline performance benchmarks measured on an NVIDIA T4 GPU:

How to Use

You can easily download and run these models using the huggingface_hub and ultralytics libraries.

Install Dependencies

pip install ultralytics huggingface_hub

1. 1.Python Inference Code

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Choose the model you want to use:
# Option 1: "yolo11n.pt"
# Option 2: "yolo26n.pt"
model_name = "yolo11n.pt"

# Download the weights directly from Hugging Face
model_path = hf_hub_download(repo_id="elvinguseinov/wildfire-detection-yolo11-yolo26-nano", filename=model_name)

# Load the model
model = YOLO(model_path)

# Run inference on an image or video
results = model.predict("path/to/test_image.jpg")

# Display results
results[0].show()