https://github.com/agneya-1402/aircraft_detector

This project implements an Aircraft Detector using YOLOv8 to detect aircraft in images. The dataset is automatically split into train, validation, and test sets, and a YOLOv8 Nano model is trained on this data.
https://github.com/agneya-1402/aircraft_detector

artificial-intelligence computer-vision opencv yolo yolov8

Last synced: about 2 months ago
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This project implements an Aircraft Detector using YOLOv8 to detect aircraft in images. The dataset is automatically split into train, validation, and test sets, and a YOLOv8 Nano model is trained on this data.

• Host: GitHub
• URL: https://github.com/agneya-1402/aircraft_detector
• Owner: agneya-1402
• License: mit
• Created: 2025-02-02T13:30:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-02T13:58:39.000Z (over 1 year ago)
• Last Synced: 2025-07-11T20:41:39.093Z (12 months ago)
• Topics: artificial-intelligence, computer-vision, opencv, yolo, yolov8
• Language: Python
• Homepage:
• Size: 49 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Aircraft Detector using YOLOv8

## Overview

This project implements an **Aircraft Detector** using **YOLOv8** to detect aircraft in images. The dataset is automatically split into **train, validation, and test sets**, and a YOLOv8 **Nano model** is trained on this data. 

 

![1](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_1.png)

![2](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_11.png)

![3](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_2.png)

![4](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_3.png)

![5](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_5.png)

## Project Structure

```

├── dataset.yaml  # YOLO dataset config

├── weights/

│   ├── best.pt  # Best trained model

│   ├── last.pt  # Last trained model

├── train_model_v2.py  # Training script

├── test_v2.py  # Inference script

├── README.md  # Project documentation

```

## Installation

Ensure you have Python 3.8+ installed. Then, install dependencies:

```bash

pip install ultralytics numpy matplotlib opencv-python 

```

## Dataset Preparation

The dataset is automatically split into **train (80%)**, **validation (10%)**, and **test (10%)** during execution.

Dataset: https://www.kaggle.com/datasets/khlaifiabilel/military-aircraft-recognition-dataset/data

## Training the Model

To train the YOLOv8 model, run:

```bash

python train_model_v2.py

```

This will:

- Load the **YOLOv8-Nano** model (`yolov8n.pt`)

- Train the model for **15 epochs** (increase to 50 if your machine can handle)

- Save trained weights in the `weights/` directory

## Testing the Model

Run inference on test images:

```bash

python test_v2.py

```

This will:

- Detect objects in all test images

- Draw bounding boxes and count objects

- Display results using Matplotlib

## Evaluation Metrics

The trained model is evaluated using:

- **Precision**

- **Recall**

- **F1 Score**

Results : ![Results](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/results.png)

F1 Curve : ![F1_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/F1_curve.png)

PRECISION Curve : ![PR_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/P_curve.png)

PRECISION RECALL Curve : ![P_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/PR_curve.png)

RECALL Curve : ![R_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/R_curve.png)

## Model Weights

The trained YOLOv8 model weights are saved in the `weights/` directory:

- **best.pt** – Best performing model checkpoint

- **last.pt** – Model from the last training epoch

## Author

**Agneya Pathare** – Robotics Engineer | AI & Computer Vision Developer