---
license: mit
tags:
  - downscaling
  - edsr
  - ERA5 - COSMO-REA6
  - wind
  - super-image
library_name: super-image
model_type: edsr
datasets:
  - your-dataset-name
---

# EDSR-DSC (4× Downscaling of Wind Velocities)

This model is a custom-trained version of the Enhanced Deep Super-Resolution (EDSR) model from the [`super-image`](https://github.com/eugenesiow/super-image) library.  
It is adapted for downscaling of **2-channel ERA5 data** (e.g., wind u and v components), by a factor of 4× (trained using **COSMO-REA6** as high-resolution data). 

---

## 🧠 Model Architecture

- **Base**: EDSR ([Lim et al. 2017](https://arxiv.org/abs/1707.02921))
- **Input channels**: 2  (U & V components of wind speed)
- **Output channels**: 2  
- **Feature channels (`n_feats`)**: 64  
- **Residual blocks**: 32  
- **Mean-shift normalization**: Removed  
- **Upsampling**: Enabled  
- **Scale factor**: 4×

---

## 📦 Files in this Repository

| File                   | Description                                   |
|------------------------|-----------------------------------------------|
| `config.json`          | Configuration for the modified EDSR model     |
| `pytorch_model_4x.pt`  | Pretrained weights for 4× upscaling           |

---

## 🚀 How to Use

```python
from super_image import EdsrModel, EdsrConfig
from huggingface_hub import hf_hub_download
import torch
import xarray as xr
import numpy as np

# load config 
config, _ = EdsrConfig.from_pretrained("lschmidt/edsr-dsc")

# load model & remove normalization
model = EdsrModel(config)
del model.sub_mean
del model.add_mean

# load pre-trained weights
state_dict_path = hf_hub_download(repo_id="lschmidt/edsr-dsc", filename="pytorch_model_4x.pt")
state_dict = torch.load(state_dict_path, map_location="cpu")
model.load_state_dict(state_dict, strict=False)

# create random input: must be a 4D tensor (B, C=2, H, W)
inputs = torch.randn(1, 2, 40, 40)  # replace with coarse wind velocity fields

# or use sample data
data_path = hf_hub_download(
    repo_id="lschmidt/edsr-dsc",
    filename="test_wind_velocities.nc",
    subfolder="test_data"  
)
ds = xr.open_dataset(data_path)
u = ds["u100"].values[0]
v = ds["v100"].values[0]
inputs = torch.from_numpy(np.stack([u, v], axis=0)).unsqueeze(0).float()  

# prediction
outputs = model(inputs)