metadata

license: agpl-3.0
library_name: ultralytics
tags:
  - object-detection
  - yolov8
  - beetle
  - insect
  - computer-vision
datasets:
  - roboflow
metrics:
  - map
model-index:
  - name: beetle-detection-yolov8
    results:
      - task:
          type: object-detection
        dataset:
          type: beetle-detection
          name: Beetle Detection Dataset
        metrics:
          - type: map
            value: 0.9763
            name: [email protected]
          - type: map
            value: 0.8956
            name: [email protected]:0.95

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 [email protected] 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

Metric	Value
[email protected]	97.63%
[email protected]:0.95	89.56%
Precision	95.2%
Recall	94.8%
Processing Time (CPU)	~100ms per image

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.

Murasan
/

beetle-detection-yolov8