Update README.md

README.md

@@ -1,24 +1,26 @@
 ---
 license: mit
 tags:
-  - image-super-resolution
-  - climate
-  - wind-power
-  - torch
-  - rcan
+  - downscaling
+  - edsr
+  - ERA5 - COSMO-REA6
+  - wind
 library_name: super-image
 inference: false
 ---
 
-# RCAN-DSC: Residual Channel Attention Network for Downscaling Wind Fields
+# RCAN-DSC (4× Downscaling of Wind Velocities)
+
+This model is a custom-trained version of the [RCAN](https://arxiv.org/abs/1807.02758) 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). 
+
 
-This model is a modified version of [RCAN](https://arxiv.org/abs/1807.02758), originally designed for image super-resolution, adapted for use in **climate downscaling** tasks. Specifically, it is configured to process **2-channel input data** representing wind vector components (u, v) at low spatial resolution and produce higher-resolution output fields.
 
 ## 🧠 Model Description
 
 - Based on the original RCAN architecture from `super-image`.
 - `sub_mean` and `add_mean` normalization layers have been **removed**
-- Supports **multi-channel inputs**, currently set up for **2-channel wind fields**.
+- Supports **multi-channel inputs**, currently set up for **2-channel wind velocity fields**.
 
 
 ## 🧪 Example
@@ -40,5 +42,8 @@ state_dict_path = hf_hub_download(repo_id="lschmidt/rcan-dsc", filename="pytorch
 state_dict = torch.load(state_dict_path, map_location="cpu")
 model.load_state_dict(state_dict, strict=False)
 
-inputs = torch.randn(1, 2, 10, 10) 
+# generate sample data (B, C, W, H) 
+inputs = torch.randn(1, 2, 10, 10)
+
+# prediction
 output = model(inputs)