metadata

license: mit
datasets:
  - IP102
library_name: ultralytics
tags:
  - object-detection
  - YOLO11s
  - pests
  - agriculture
  - ip102
model-index:
  - name: IP102 Pest Detector (YOLO11 Small)
    results:
      - task:
          type: object-detection
        dataset:
          name: IP102
          type: pest-detection
        metrics:
          - type: mAP@0.5
            value: 0.941
          - type: mAP@0.5:0.95
            value: 0.838
          - type: Precision
            value: 0.923
          - type: Recall
            value: 0.907

🐞 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, 34K+ images)
Image Sizes: Trained on 640x640 and 896x896
Classes: 102 pest species
Framework: Ultralytics YOLO11s
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
mAP@0.5	0.941	0.815
mAP@0.5:0.95	0.838	0.605

🐜 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.

📦 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()