Murasan/beetle-detection-yolov8

YOLOv8 Beetle Detection Model

Model Description

This is a YOLOv8-based object detection model fine-tuned for beetle detection. The model was trained on a custom dataset of 500 beetle images from Roboflow and achieves excellent performance with mAP@0.5 of 97.63%.

Model Details

• Base Model: YOLOv8n (nano) from Ultralytics
• Task: Object Detection
• Classes: 1 (beetle)
• Input Size: 640x640 pixels
• Framework: PyTorch
• License: AGPL-3.0 (inherited from YOLOv8)

Performance Metrics

Dataset

• Source: Roboflow Universe
• License: CC BY 4.0
• Images: 500 annotated beetle images
• Split: 80% train, 15% validation, 5% test
• Augmentations: Applied during training for robustness

Usage

Installation

pip install ultralytics

Python Inference

from ultralytics import YOLO
import cv2

# Load the model
model = YOLO('best.pt')

# Run inference
results = model('path/to/image.jpg')

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        # Get coordinates and confidence
        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
        confidence = box.conf[0].cpu().numpy()
        
        print(f"Beetle detected with confidence: {confidence:.2f}")
        print(f"Bounding box: ({x1}, {y1}, {x2}, {y2})")

Command Line

yolo predict model=best.pt source='path/to/image.jpg'

Training Details

• Epochs: 100
• Batch Size: 16
• Optimizer: AdamW
• Learning Rate: 0.01 (initial)
• Hardware: Google Colab GPU
• Training Time: ~2 hours

Applications

This model is designed for:

• Agricultural monitoring
• Entomological research
• Biodiversity studies
• Educational purposes
• IoT-based pest detection systems

Limitations

• Trained specifically on beetle images
• Performance may vary with different lighting conditions
• Best results with clear, well-lit images
• Single class detection only

Model Files

• best.pt: PyTorch model weights (recommended)
• best.onnx: ONNX format for cross-platform deployment

Citation

If you use this model in your research, please cite:

@model{beetle-detection-yolov8,
  title={YOLOv8 Beetle Detection Model},
  author={Insect Detection Training Project},
  year={2025},
  url={https://huggingface.co/Murasan/beetle-detection-yolov8}
}

License

This model is licensed under AGPL-3.0, inherited from the original YOLOv8 implementation by Ultralytics.

Base Model Attribution

• YOLOv8: Ultralytics YOLOv8
• Original License: AGPL-3.0
• Paper: YOLOv8: A Real-Time Object Detection Algorithm

Related Projects

• Base Training Repository
• Hailo 8L Deployment Guide

Contact

For questions or issues, please open an issue in the base repository.