odiug77/wildfire-smoke-fire

Wildfire Smoke and Flame Detection – YOLOv26m (Updated)

Overview

This model is based on the YOLOv26m architecture and has been specifically fine-tuned to detect wildfire smoke and flames in outdoor environments.

Compared to previous versions, this update utilizes a larger dataset and optimized training hyperparameters, resulting in a significant boost in Recall and mAP. It is designed for early wildfire detection using standard RGB surveillance cameras positioned at strategic observation points such as hills, fire towers, or remote monitoring stations.

🎯 Purpose

The primary goal of this model is early wildfire detection, enabling faster response times and reducing environmental and economic damage.

Optimized for:

• Daytime & Nighttime wildfire detection.
• Smoke Recognition: Critical for long-distance early detection.
• Flame Detection: For immediate confirmation of fire events.
• Standard RGB Imagery: Works with standard optical cameras (no thermal sensors required).

🧠 Model Architecture

• Architecture: YOLOv26m (fused)
• Task: Object Detection
• Classes: smoke, fire
• Layers: 132
• Parameters: 20,350,994
• GFLOPs: 67.9
• Input Resolution: 640x640 (RGB)

📊 Model Performance (Latest Validation)

The model was trained for 80 epochs using Automatic Mixed Precision (AMP) and AdamW optimizer.

Overall Metrics (Validation Set)

Metric	Value	Improvement vs Previous
Precision (P)	0.635	+0.3%
Recall (R)	0.681	+17.2%
mAP @0.50	0.675	+13.6%
mAP @0.50:0.95	0.425	+11.9%

Per-Class Metrics

Class	Images	Instances	Precision	Recall	mAP@0.50	mAP@0.50:0.95
Smoke	180	253	0.633	0.767	0.752	0.491
Fire	123	836	0.637	0.596	0.598	0.359

Inference Speed (Tesla T4 GPU)

• Pre-process: 0.3 ms
• Inference: 12.3 ms
• Post-process: 0.8 ms
• Total Throughput: ~74 FPS (theoretical)

🚀 Inference Example (CPU)

Minimal example to run inference on CPU and print detection results.

from ultralytics import YOLO

# Load the trained model
model = YOLO("wildfire-smoke-fire.pt")

# Run inference on an image
results = model("forest_view.jpg", conf=0.25)

# Process results
for r in results:
    print(f"Detected {len(r.boxes)} objects.")
    r.show()  # Display annotated image

🚀 Usage Example

Install the required library:

pip install ultralytics

🛠️ Training Configuration

• Optimizer: AdamW (lr=0.001667, momentum=0.9)
• Augmentations: Blur, MedianBlur, ToGray, CLAHE
• Environment: PyTorch 2.1.0+cu128 on Tesla T4