DamageTriageFormer-model

Damage-TriageFormer (primary crop-mode checkpoint)

Current revision checkpoint. This is the primary crop-mode model reported in the revised manuscript. It was trained from scratch on the corrected photo-atomic split, which keeps every tile from a source photo in one fold. It replaces the previously published tile-mode checkpoint trained on the superseded tile-level split.

Damage-TriageFormer predicts a five-class building damage typology from a single post-event RGB image. Each input is a crop centered on one footprint-derived building instance. The crop includes a 32-pixel context margin and is resized to 224×224 pixels.

Files

• best.pth — the primary revised checkpoint, selected by validation macro F1. It is a PyTorch state_dict containing the full fine-tuned backbone and all three prediction heads.
• config.json — architecture, preprocessing, training, split, and result metadata for this checkpoint.

Checkpoint SHA-256: 49761e951f52d7ee14eaf9f7f4e4d8a81e5daaaee04d285dd55004076bcf9430.

Architecture

The model fine-tunes a DINOv3 ViT-L/16 backbone end to end. The class-token embedding feeds three linear heads:

1. an any-damage gate;
2. a four-way damaged-class leaf head; and
3. an auxiliary severity-regression head used only during training.

The five-class probability distribution is formed by combining the gate and conditional leaf probabilities. Unlike the tile-mode architecture, this checkpoint does not use a Simple Feature Pyramid or mask pooling. Building footprints are used to define the input crops before inference.

Preprocessing

• Start from a footprint-derived building bounding box in a 1024×1024 tile.
• Add 32 pixels of context on each side, clipped to the tile boundary.
• Resize the crop to 224×224 pixels.
• Convert to RGB and normalize with ImageNet mean [0.485, 0.456, 0.406] and standard deviation [0.229, 0.224, 0.225].
• Exclude labeled footprint fragments smaller than 30 pixels.

Training

• Split: corrected photo-atomic DamageTriage-Bench split
• Epochs: 20
• Optimizer: AdamW
• Learning rate: 3e-5
• Weight decay: 1e-4
• Batch size: 48 on one A100 40 GB GPU
• Schedule: cosine annealing
• Seed: 42
• Augmentation: random horizontal and vertical flips
• Gate loss: binary cross-entropy
• Leaf loss: inverse-square-root class-weighted cross-entropy, weight 2.0, label smoothing 0.1
• Auxiliary loss: Smooth L1, weight 0.5, targets [0.0, 0.3, 0.7, 0.5, 1.0]

Results

The checkpoint was selected on the validation fold. The test fold was not used for optimization or checkpoint selection.

These are single-seed, within-event results. The paper reports substantially weaker transfer to held-out disasters and discusses the small support and label ambiguity of the roof-damage classes.

Usage

Use the crop-mode script in the companion code repository: github.com/YimingXiao98/Damage-TriageFormer. The script defines the exact model class and preprocessing pipeline used for this checkpoint.

git clone https://github.com/YimingXiao98/Damage-TriageFormer.git
cd Damage-TriageFormer

export DINOV3_RUNS_ROOT=/path/to/runs
mkdir -p "$DINOV3_RUNS_ROOT/crop_dtf"

hf download Ymx1025/DamageTriageFormer-model best.pth \
  --local-dir "$DINOV3_RUNS_ROOT/crop_dtf"

python scripts/train_crop.py \
  --arch dinov3_vitl16 --gated --dump-only \
  --crops /path/to/prepared/crops \
  --index-dir instance_index \
  --splits photo_splits.json \
  --batch 48 --name crop_dtf

The checkpoint contains a raw PyTorch state dictionary. Loading it requires the GatedDinoCrop architecture defined in scripts/train_crop.py.

Training data

DamageTriage-Bench (CC-BY-NC-4.0), using the released photo-atomic split.

Intended use and limitations

This release supports research and reproducibility. It is not a substitute for engineering inspection and has not been validated for autonomous habitability, safety, inspection-priority, or resource-allocation decisions. It assumes that registered building footprints are available. The three study events do not establish generalization to a new disaster, and all reported configurations use a single seed. Rare and visually ambiguous roof-damage classes remain the least reliable.

License

Released under CC-BY-NC-4.0, consistent with DamageTriage-Bench, and subject to the redistribution terms of the underlying NOAA Emergency Response Imagery and source building-footprint layers.

Citation

@misc{xiao2026damagetriageformerfoundationmodelframeworktypologybased,
      title={Damage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery}, 
      author={Yiming Xiao and Yu-Hsuan Ho and Sanjay Thasma and Junwei Ma and Ali Mostafavi},
      year={2026},
      eprint={2606.12248},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.12248}, 
}