QuincySorrentino/AeroYOLO

Aircraft & Drone Detector - YOLO11n

YOLO11n model trained to detect aircraft, helicopters, and drones in images.

Model Details

• Architecture: YOLO11n (nano)
• Classes: 3 (aircraft, drone, helicopter)
• Input Size: 640x640
• Parameters: 2.59M
• Framework: Ultralytics

Performance Metrics

Metric	Value
mAP50-95	0.703
mAP50	0.966
Precision	0.924
Recall	0.941

Usage

Installation

pip install ultralytics huggingface_hub

Inference

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Download model
model_path = hf_hub_download(
    repo_id=QuincySorrentino/AeroYOLO,
    filename="best.pt"
)

# Load and run inference
model = YOLO(model_path)
results = model.predict('path/to/image.jpg', conf=0.25)

# Display results
results[0].show()

Batch Processing

# Process multiple images
results = model.predict('path/to/images/', save=True, conf=0.25)

# Results saved to runs/detect/predict/

Classes

• 0: aircraft
• 1: drone
• 2: helicopter

Training Details

• Dataset: 10,799 training images
• Validation: 603 images
• Epochs: 100
• Batch Size: Auto (58)
• Image Size: 640x640
• Augmentation: Auto-augment, mosaic, mixup

Citation

@software{yolo11_ultralytics,
  author = {Glenn Jocher and Jing Qiu},
  title = {Ultralytics YOLO11},
  version = {11.0.0},
  year = {2024},
  url = {https://github.com/ultralytics/ultralytics}
}