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README.md

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----
-license: agpl-3.0
----
+---
+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
+
+```bash
+pip install ultralytics
+```
+
+### Python Inference
+
+```python
+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
+
+```bash
+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:
+
+```bibtex
+@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](https://github.com/ultralytics/ultralytics)
+- **Original License**: AGPL-3.0
+- **Paper**: [YOLOv8: A Real-Time Object Detection Algorithm](https://arxiv.org/abs/2305.09972)
+
+## Related Projects
+
+- [Base Training Repository](https://github.com/Murasan201/insect-detection-training)
+- [Hailo 8L Deployment Guide](https://github.com/Murasan201/insect-detection-training/blob/main/HAILO_DEPLOYMENT_GUIDE.md)
+
+## Contact
+
+For questions or issues, please open an issue in the [base repository](https://github.com/Murasan201/insect-detection-training/issues).

best.onnx

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+size 12251052

best.pt

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+oid sha256:91cec6142a7e08ecf632bdb8990f669df46c7b78c54fa1ba822c512d0a89c976
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config.json

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+{
+  "model_type": "yolov8",
+  "framework": "ultralytics",
+  "task": "object-detection",
+  "classes": 1,
+  "class_names": ["beetle"],
+  "input_size": [640, 640],
+  "model_size": "nano",
+  "base_model": "yolov8n.pt",
+  "training": {
+    "epochs": 100,
+    "batch_size": 16,
+    "dataset_size": 500,
+    "train_split": 0.8,
+    "val_split": 0.15,
+    "test_split": 0.05
+  },
+  "performance": {
+    "map_50": 0.9763,
+    "map_50_95": 0.8956,
+    "precision": 0.952,
+    "recall": 0.948,
+    "processing_time_cpu_ms": 100
+  },
+  "license": "agpl-3.0",
+  "dataset_license": "cc-by-4.0"
+}