Drone Detector with YOLO11n
This model was originally made using YOLO8n for a project at my uni, but I have since trained it for longer, and I've upgraded the base model to YOLO11.
- Model Architecture: YOLOv11n
- Task: Object Detection (Drone Detection)
- Image Type: Dataset from Maciej Pawełczyk and Marek Wojtyra
- Classes: 1 (drone)
Training Validation Results
Training and Validation Losses
Confusion Matrix
Precision-Recall Curve
F1 Score Curve
Training Configuration
- Model weight file:
yolo11n_drone.pt - Base model: ultralytics/yolov11n
- Number of Epochs: 150
- Image Size: 640x640
Deployment
How to use this model
from ultralytics import YOLO
# Load the model
model = YOLO("yolo11n_drone.pt")
Limitations
Honestly, this model is good at recognizing drones, but it struggles with limiting the size of the box where it thinks it is. You can train it further if you want, but ¯\_(ツ)_/¯. From experience, I can say that it struggles a bit at a distance, but it works better than I thought it would.
Disclaimer
I would like to direct all possible credit to Pawełczyk and Wojtyra for their dataset. I am however in no way, shape or form related to them in any function. Here is the link if you want to cite their paper. Here is a link to the Github repo where I originally found their dataset.
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Model tree for marie-kjelberg/drone-detector
Base model
Ultralytics/YOLO11