🐞 IP102 Pest Detector — YOLO11 Small
A custom YOLO11 object detection model trained on the IP102 dataset — designed for pest detection in precision agriculture.
Model Purpose: Detect and classify 102 pest species in real-time field conditions using computer vision.
💡 Model Details
- Model: YOLO11 Small
- Dataset: IP102 (Balanced, 14K+ images)
- Image Sizes: Trained on 640x640 and 896x896
- Classes: 102 pest species
- Framework: Ultralytics YOLOv8
- Hardware: NVIDIA A100 GPU
- Epochs: 77
- License: MIT License
🧪 Performance
| Metric | Train Set | Validation Set |
|---|---|---|
| Precision | 0.912 | 0.744 |
| Recall | 0.923 | 0.789 |
| [email protected] | 0.941 | 0.815 |
| [email protected]:0.95 | 0.838 | 0.605 |
📦 Usage
from ultralytics import YOLO
# Load model
model = YOLO("path/to/best.pt")
# Run inference
results = model.predict("your_image.jpg", imgsz=640)
# Display results
results.show()
🐜 Class List
The model detects 102 agricultural pests, including:
rice leaf roller
paddy stem maggot
brown plant hopper
aphids
mole cricket
blister beetle
...and many more!
(See pests.yaml for the full class list.)
⚖️ License
This project is released under the MIT License — free for personal and commercial use.
📚 Citation
If you use this model in research or production, please cite the IP102 dataset:
Wu, S., Zhan, C., et al.
"IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition."
CVPR, 2019.
💬 Questions?
Open an issue or reach me on Hugging Face Discussions.