tornadonet-checkpoints

TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision

TornadoNet is a comprehensive benchmark for automated street-level building damage assessment following tornado events. It evaluates modern real-time object detection architectures, comparing CNN-based YOLO models with transformer-based RT-DETR models. The project introduces ordinal-aware supervision strategies to improve multi-level damage classification (DS0-DS4) under realistic post-disaster conditions.

• Paper: TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision
• GitHub Repository: crumeike/TornadoNet
• Dataset: tornadonet-datasets

Key Features

• Standardized Benchmark: Evaluates YOLOv8, YOLO11, and RT-DETR using 3,333 high-resolution images and 8,890 building instances from the 2021 Midwest tornado outbreak.
• Ordinal Supervision: Introduces soft ordinal targets and distance penalties, improving damage severity estimation by 4.8% mAP for RT-DETR models.
• Real-Time Performance: Optimized for high throughput, with variants reaching up to 276 FPS on NVIDIA A100 GPUs.
• Five-Level Classification: Based on the IN-CORE damage states (DS0 to DS4).

Sample Usage

This model can be used with the ultralytics library.

Installation

pip install ultralytics huggingface_hub

Inference

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download a specific model checkpoint (e.g., YOLO11-x baseline)
model_path = hf_hub_download(
    repo_id="crumeike/tornadonet-checkpoints",
    filename="tornadonet-yolo11-x-baseline/best.pt"
)

# Load model
model = YOLO(model_path)

# Run inference
results = model.predict(
    source="path/to/image.jpg",
    imgsz=896,
    conf=0.25
)

# Visualize results
results[0].show()

Damage Classification Levels

The model predicts damage states according to the following framework:

Citation

@article{umeike2026tornadonet,
  title={TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision},
  author={Umeike, Robinson and Pham, Cuong and Hausen, Ryan and Dao, Thang and Crawford, Shane and Brown-Giammanco, Tanya and Lemson, Gerard and van de Lindt, John and Johnston, Blythe and Mitschang, Arik and Do, Trung},
  journal={arXiv preprint arXiv:2603.11557},
  year={2026}
}