{"id":24946469,"url":"https://github.com/agneya-1402/aircraft_detector","last_synced_at":"2026-05-16T11:34:25.512Z","repository":{"id":275433484,"uuid":"926068019","full_name":"agneya-1402/Aircraft_Detector","owner":"agneya-1402","description":"This project implements an Aircraft Detector using YOLOv8 to detect aircraft in images. 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The dataset is automatically split into **train, validation, and test sets**, and a YOLOv8 **Nano model** is trained on this data. \n \n![1](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_1.png)\n![2](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_11.png)\n![3](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_2.png)\n![4](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_3.png)\n![5](https://github.com/agneya-1402/Aircraft_Detector/blob/main/demo_outputs/Figure_5.png)\n\n## Project Structure\n```\n├── dataset.yaml  # YOLO dataset config\n├── weights/\n│   ├── best.pt  # Best trained model\n│   ├── last.pt  # Last trained model\n├── train_model_v2.py  # Training script\n├── test_v2.py  # Inference script\n├── README.md  # Project documentation\n```\n\n## Installation\nEnsure you have Python 3.8+ installed. Then, install dependencies:\n```bash\npip install ultralytics numpy matplotlib opencv-python \n```\n\n## Dataset Preparation\nThe dataset is automatically split into **train (80%)**, **validation (10%)**, and **test (10%)** during execution.\nDataset: https://www.kaggle.com/datasets/khlaifiabilel/military-aircraft-recognition-dataset/data\n\n## Training the Model\nTo train the YOLOv8 model, run:\n```bash\npython train_model_v2.py\n```\nThis will:\n- Load the **YOLOv8-Nano** model (`yolov8n.pt`)\n- Train the model for **15 epochs** (increase to 50 if your machine can handle)\n- Save trained weights in the `weights/` directory\n\n## Testing the Model\nRun inference on test images:\n```bash\npython test_v2.py\n```\nThis will:\n- Detect objects in all test images\n- Draw bounding boxes and count objects\n- Display results using Matplotlib\n\n## Evaluation Metrics\nThe trained model is evaluated using:\n- **Precision**\n- **Recall**\n- **F1 Score**\n\nResults : ![Results](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/results.png)\nF1 Curve : ![F1_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/F1_curve.png)\nPRECISION Curve : ![PR_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/P_curve.png)\nPRECISION RECALL Curve : ![P_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/PR_curve.png)\nRECALL Curve : ![R_curve](https://github.com/agneya-1402/Aircraft_Detector/blob/main/outputs/R_curve.png)\n\n\n## Model Weights\nThe trained YOLOv8 model weights are saved in the `weights/` directory:\n- **best.pt** – Best performing model checkpoint\n- **last.pt** – Model from the last training epoch\n\n## Author\n**Agneya Pathare** – Robotics Engineer | AI \u0026 Computer Vision Developer\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagneya-1402%2Faircraft_detector","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagneya-1402%2Faircraft_detector","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagneya-1402%2Faircraft_detector/lists"}