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README.md

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+---
+license: mit
+language: en
+tags:
+  - domain-adaptation
+  - disaster-assessment
+  - resnet50
+  - image-classification
+  - unsupervised-domain-adaptation
+datasets:
+  - abalhomaid/disaster-damage-assessment
+---
+
+# UDA Disaster Damage Assessment - Model Weights
+
+Pre-trained model weights for reproducing the experiments in:
+
+> **Unsupervised Domain Adaptation for Rapid Disaster Damage Assessment**
+
+These checkpoints reproduce Tables 1-6 in the paper.
+
+## Models Included
+
+| Model | Count | Description |
+|-------|-------|-------------|
+| ResNet50 (source-only) | 12 | State dict files for all 12 source-target domain pairs |
+| CDAN | 36 | 12 tasks x 3 seeds (SWD=0) |
+| CORAL | 36 | 12 tasks x 3 seeds (SWD=0) |
+| MMD | 36 | 12 tasks x 3 seeds (SWD=0) |
+| DANN | 36 | 12 tasks x 3 seeds (SWD=0) |
+| AWDAN (best) | 36 | 12 tasks x 3 seeds (best SWD config per task, selected by max F1) |
+| **Total** | **192 files** | **~19.8 GB** |
+
+## Domains
+
+| Domain | Code | Event | Images |
+|--------|------|-------|--------|
+| Ecuador Earthquake | E | 2016 | 1,724 |
+| Nepal Earthquake | N | 2015 | 19,102 |
+| Hurricane Matthew | M | 2016 | 333 |
+| Typhoon Ruby | R | 2014 | 833 |
+
+All 12 source-to-target combinations (E2M, E2N, E2R, M2E, M2N, M2R, N2E, N2M, N2R, R2E, R2M, R2N) are evaluated with seeds {2, 32, 128}.
+
+## Download
+
+```bash
+pip install huggingface_hub
+huggingface-cli download abalhomaid/disaster-uda-models --local-dir .
+```
+
+This places files into the correct directory structure expected by the evaluation scripts.
+
+## File Structure
+
+```
+models/resnet50/c4/
+    {S}_{T}_model_epoch_best_statedict.pth    # Source-only baseline (12 files)
+
+train_jobs/cdan/logs/{model}/seed_{seed}/
+    Damage_{S}2{T}_SWD_{swd}_trade_offs_{to}/
+        checkpoints/best.pth                   # DA model checkpoint
+```
+
+Where `{model}` is one of: `cdan`, `coral`, `mmd`, `dann`.
+
+## Usage
+
+Clone the reproduction repository and download the weights:
+
+```bash
+git clone https://github.com/abalhomaid/disaster-assesment.git
+cd disaster-assesment
+git checkout reproducibility
+
+# Download model weights
+huggingface-cli download abalhomaid/disaster-uda-models --local-dir .
+
+# Set up evaluation environment
+conda env create -f tllib_metric.yaml
+conda activate tllib_metric
+pip install -e .
+
+# Evaluate a model (e.g., DANN E->M, seed=32)
+PYTHONPATH="$PWD:$PWD/examples/domain_adaptation/image_classification" \
+python examples/domain_adaptation/image_classification/dann.py \
+  data/damage -d Damage -s E -t M -a resnet50 --seed 32 --scratch \
+  --log train_jobs/cdan/logs/dann/seed_32/Damage_E2M_SWD_0_trade_offs_1 \
+  --phase test
+```
+
+See the [repository README](https://github.com/abalhomaid/disaster-assesment/tree/reproducibility) for full reproduction instructions.
+
+## Expected Results (Table 5 Averages)
+
+| Model | Accuracy | Precision | Recall | F1 |
+|-------|----------|-----------|--------|-----|
+| Source-only | 75.2 | 84.1 | 70.0 | 75.6 |
+| CDAN | 81.0 | 83.6 | 83.1 | 83.2 |
+| CORAL | 79.5 | 82.2 | 81.7 | 81.8 |
+| MMD | 81.1 | 83.8 | 83.0 | 83.2 |
+| DANN | 80.6 | 83.9 | 81.9 | 82.7 |
+| **AWDAN** | **81.9** | 83.9 | **85.0** | **84.2** |
+
+## Architecture
+
+All models use ResNet50 backbone with 2-class output (damage / no damage), input size 224x224.
+
+- **Source-only**: Standard ResNet50 fine-tuned on source domain only
+- **DANN/CDAN**: ResNet50 + domain discriminator (adversarial DA)
+- **CORAL/MMD**: ResNet50 + divergence minimization (domain generalization)
+- **AWDAN**: DANN + Sliced Wasserstein Distance regularization in label space