WindFormer

Developed for the ESAWAAI Project and ESAWAAI Legacy Dataset HF Bucket

A PyTorch implementation that loads a pre-trained WindFormerDist architecture and prepares it in evaluation mode for inference.

Requirements

Ensure the necessary dependencies are installed:

pip install torch safetensors

Note: CUDA is utilized automatically when available; otherwise, execution defaults to the CPU.

File Structure

File	Description
main.py	Script to load the model weights and set it to evaluation mode
windformer.py	Model architecture definition for WindFormerDist
model.safetensors	Pre-trained model weights

Execution

To run the model inference pipeline:

python main.py

Model Weights & Configuration

Weights are stored using Safetensors.

Important: The instantiation parameters (e.g., WindFormerDist(fusion=False)) must strictly match the configuration used during training. Any discrepancy will cause load_state_dict to fail due to missing or unexpected keys.

Cite

Benchaabane, A., Toft, L. D. D. S., Ristea, N.-C., Dimitriadou, K., Husson, R., Hasager, C. B., Anghel, A., Longépé, N., Mouche, A., Grouazel, A., & Datcu, M. (2026). Explainable SAR measurements for Wind Assessment with Artificial Intelligence (ESAWAAI).

License

This dataset and its associated documentation are released under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0) license.