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%.
- 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)
| Metric | Value | |--------|-------| | [email protected] | 97.63% | | [email protected]:0.95 | 89.56% | | Precision | 95.2% | | Recall | 94.8% | | Processing Time (CPU) | ~100ms per image |
- 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
- Epochs: 100 - Batch Size: 16 - Optimizer: AdamW - Learning Rate: 0.01 (initial) - Hardware: Google Colab GPU - Training Time: ~2 hours
This model is designed for: - Agricultural monitoring - Entomological research - Biodiversity studies - Educational purposes - IoT-based pest detection systems
- Trained specifically on beetle images - Performance may vary with different lighting conditions - Best results with clear, well-lit images - Single class detection only
- `best.pt`: PyTorch model weights (recommended) - `best.onnx`: ONNX format for cross-platform deployment
If you use this model in your research, please cite:
This model is licensed under AGPL-3.0, inherited from the original YOLOv8 implementation by Ultralytics.
- YOLOv8: Ultralytics YOLOv8 - Original License: AGPL-3.0 - Paper: YOLOv8: A Real-Time Object Detection Algorithm
- Base Training Repository - Hailo 8L Deployment Guide
For questions or issues, please open an issue in the base repository.