This repository contains a YOLO-based model for aerial object detection. The model is trained to detect and classify various objects from aerial images, such as planes, ships, vehicles, and more. Below are the details and visualizations regarding the performance of the model.
Model Description
Model Name: YOLO Aerial Mine Detection
Framework: Ultralytics YOLOv11n-obb
Languages: English, Persian
Classes Detected:
Plane (هواپیما)
Ship (کشتی)
Storage Tank (مخزن ذخیره)
Baseball Diamond (زمین بیسبال)
Tennis Court (زمین تنیس)
Basketball Court (زمین بسکتبال)
Ground Track Field (زمین دو و میدانی)
Harbor (بندرگاه)
Bridge (پل)
Large Vehicle (خودرو بزرگ)
Small Vehicle (خودرو کوچک)
Helicopter (هلیکوپتر)
Roundabout (میدان)
Soccer Ball Field (زمین فوتبال)
Swimming Pool (استخر شنا)
Training Details
Dataset: Custom aerial images annotated for object detection.
Metrics: Precision, Recall, mAP@0.5, F1 Score
Training Environment: Kaggle, GPU-accelerated environment
Optimizer: SGD
Libraries Used:
Ultralytics: YOLOv11n-obb (version 8.0.0)
Gradio: For creating the user interface (version 3.1.4)
Pandas: For data handling (version 1.3.3)
Pillow: For image manipulation (version 8.4.0)
OpenCV: For video processing (version 4.5.3)
Evaluation Results
Below are the various evaluation results obtained during the training and testing phases of the model.
F1-Confidence Curve
F1-Confidence Curve
Precision-Confidence Curve
Precision-Confidence Curve
Precision-Recall Curve
Precision-Recall Curve
Recall-Confidence Curve
Recall-Confidence Curve
Confusion Matrix
Confusion Matrix
Labels Correlogram
Labels Correlogram
Labels Distribution
Labels Distribution
How to Use
Clone this repository.
Load the model using the Ultralytics YOLO library.
Use the model for object detection on aerial images.
python
from ultralytics import YOLO
Load the trained model
yolo_model = YOLO('yolo11n-obb.pt')
Perform detection
yolo_model('test_image.jpg')
License
This model is open-sourced under the MIT License.
Acknowledgements
Special thanks to the Kaggle community and Hugging Face for providing tools and platforms for developing and sharing this project.
