Update README.md

README.md

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-short_description: Official Repository of Pretrained Models on BigEarthNet v2.0
+short_description: Repository of Pretrained Model Weights on BigEarthNet v2.0
 ---
 
 [TU Berlin](https://www.tu.berlin/) | [RSiM](https://rsim.berlin/) | [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) | [BigEarth](http://www.bigearth.eu/) | [BIFOLD](https://bifold.berlin/)
 :---:|:---:|:---:|:---:|:---:
 <a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/>  |  <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> | <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> | <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> | <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo">
 
-# BigEarthNet v2.0 Pretrained Weights
-We provide pretrained weights for several different models. 
-The weights for the best-performing model, based on the Macro Average Precision score on the recommended test split, have been uploaded.
-All models have been trained using: i) Sentinel-1 data only (S1), ii) Sentinel-2 data only (S2), or iii) both Sentinel-1 and Sentinel-2 (S1+S2) modalities together.
+# BigEarthNet v2.0 Pretrained Model Weights
+We provide weights for several different pretrained models. 
+The model weights for the best-performing model, based on the macro average precision score on the recommended test split, have been uploaded.
+All models have been trained using: i) BigEarthNet-S1 data only (S1), ii) BigEarthNet-S2 data only (S2), or iii) both BigEarthNet-S1 and -S2 (S1+S2) together.
 
 The following bands were used to train the models:  
-- For models using Sentinel-1 only: Sentinel-1 bands `["VH", "VV"]`  
-- For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]`  
-- For models using Sentinel-1 and Sentinel-2: Sentinel-2 10m bands and 20m bands and Sentinel-1 bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]`
+- For models using BigEarthNet-S1 only: Sentinel-1 bands `["VH", "VV"]`  
+- For models using BigEarthNet-S2 only: Sentinel-2 10m bands and 20m bands `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]`  
+- For models using BigEarthNet-S1 and -S2: Sentinel-2 10m bands and 20m bands and Sentinel-1 bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]`
 
 
 The multi-hot encoded output of the model indicates the predicted multi-label output.
@@ -38,7 +38,7 @@ The multi-hot encoded output relates to the following class labels sorted in alp
 
 ## Links
 
-| Model            | Equivalent [`timm`](https://huggingface.co/docs/timm/en/index) model name  |                                         Sentinel-1 only                                         |                                         Sentinel-2 only                                         |                                      Sentinel-1 and Sentinel-2                                      |
+| Model            | Equivalent [`timm`](https://huggingface.co/docs/timm/en/index) model name  |                                         S1 only                                                 |                                         S2 only                                                 |                                      S1+S2                                                          |
 |:-----------------|:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------:|
 | ConvMixer-768/32 | `convmixer_768_32`                                                         | [ConvMixer-768/32 S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s1-v0.1.1) | [ConvMixer-768/32 S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s2-v0.1.1) | [ConvMixer-768/32 S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-all-v0.1.1) |
 | ConvNext v2 Base | `convnextv2_base`                                                          | [ConvNext v2 Base S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s1-v0.1.1)  | [ConvNext v2 Base S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s2-v0.1.1)  | [ConvNext v2 Base S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-all-v0.1.1)  |
@@ -54,7 +54,7 @@ The multi-hot encoded output relates to the following class labels sorted in alp
 ## Usage
 
 To use the model, download the codes that define the model architecture from the
-[official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model 
+[official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model with the corresponding weights
 using the code below. Note that [`configilm`](https://pypi.org/project/configilm/) is a requirement to use the 
 code below.