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README.md

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-license: mit
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+---
+license: mit
+datasets:
+- CEMAC/synthetic_lee_waves
+metrics:
+- mse
+pipeline_tag: image-to-image
+library_name: fastai
+tags:
+- climate
+---
+
+# Model Card for LeeWaveNet
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+<!-- Provide a quick summary of the model. -->
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+This repository contains four neural-network models, trained using [fastai](https://docs.fast.ai/), for detecting and determining characteristics of trapped lee waves using maps of 700 hPa vertical velocity as input.
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+* The base model [segmodel.pkl](https://huggingface.co/CEMAC/LeeWaveNet/blob/main/segmodel.pkl) generates a segmentation mask indicating where trapped lee waves are present. This model uses a U-Net architecture with Resnet-34 (pre-trained on ImageNet) as the encoder model.
+* Three alternative model heads have been trained on synthetic data: [amplitude_0.0625.pkl](https://huggingface.co/CEMAC/LeeWaveNet/blob/main/amplitude_0.0625.pkl), [wavelength_0.125.pkl](https://huggingface.co/CEMAC/LeeWaveNet/blob/main/wavelength_0.125.pkl) and [orientation_0.25.pkl](https://huggingface.co/CEMAC/LeeWaveNet/blob/main/orientation_0.25.pkl). These predict the amplitude, wavelength and orientation of detected waves respectively.
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+For full details, please see the article by [Coney et al. (2023)](https://doi.org/10.1002/qj.4592).
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