Update README.md

README.md

@@ -17,17 +17,9 @@ This model is a modified version of [RCAN](https://arxiv.org/abs/1807.02758), or
 ## 🧠 Model Description
 
 - Based on the original RCAN architecture from `super-image`.
-- `sub_mean` and `add_mean` normalization layers have been **removed**, as normalization of physical variables differs from image RGB workflows.
+- `sub_mean` and `add_mean` normalization layers have been **removed**
 - Supports **multi-channel inputs**, currently set up for **2-channel wind fields**.
-- Custom head and tail layers allow flexible channel dimensions and upscaling factors (e.g., ×2, ×4).
-- Optional partial weight loading from pretrained RCAN (trained on DIV2K) to initialize the main body.
 
-## 🛰️ Intended Use
-
-This model is intended for:
-- **Super-resolution of wind fields** in climate modeling applications.
-- **Downscaling wind speed or wind power-related variables** from coarse-resolution reanalysis or GCM output.
-- Research on generalizing deep learning architectures to geoscience and Earth system science domains.
 
 ## 🧪 Example
 
@@ -38,11 +30,8 @@ import torch
 
 path = hf_hub_download(repo_id="lschmidt/rcan-dsc", filename="rcan_model.py")
 
-# Dynamically load it
 exec(open(path).read())
 
-
-# Now you can call the function directly
 model = load_rcan()
 
 # load config
@@ -53,6 +42,5 @@ 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)
+inputs = torch.randn(1, 2, 10, 10) 
 output = model(inputs)
-