Update README.md

README.md

@@ -13,18 +13,18 @@ short_description: Official Repository of Pretrained Models on BigEarthNet v2.0
 :---:|:---:|:---:|:---:|:---:
 <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">
 
-# BigEarthNetv2.0 Pretrained Weights
+# BigEarthNet v2.0 Pretrained Weights
 We provide pretrained weights for several different models. 
-All models were trained with different seeds.
-The weights for the best-performing model (based on Macro Average Precision on the recommended test split) are uploaded.
-All models are available as versions using Sentinel-1 only, Sentinel-2 only or Sentinel-1 and Sentinel-2 data.
+The weights for the best-performing model based on the Macro Average Precision score on the recommended test split are uploaded.
+All models are available in three different configurations: Using Sentinel-1 only (S1), Sentinel-2 only (S2) or both Sentinel-1 and Sentinel-2 (S1+S2) modalities together.
 
-The order of bands is as follows:  
+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"]`
 
-The output classes are always in alphabetical order:  
+The multi-hot encoded output of the model indicates the predicted multi-class label.
+The multi-hot encoded output relates to the following classes sorted in alphabetical order:  
 ['Agro-forestry areas', 'Arable land', 'Beaches, dunes, sands', 'Broad-leaved forest', 'Coastal wetlands', 
 'Complex cultivation patterns', 'Coniferous forest', 'Industrial or commercial units', 'Inland waters', 
 'Inland wetlands', 'Land principally occupied by agriculture, with significant areas of natural vegetation', 
@@ -37,20 +37,21 @@ The output classes are always in alphabetical order:
 
 | Model            | equivalent [`timm`](https://huggingface.co/docs/timm/en/index) model name  | Sentinel-1 only                                                                     | Sentinel-2 only                                                                     | Sentinel-1 and Sentinel-2                                                            |
 |:-----------------|:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
-| ConvMixer-768/32 | `convmixer_768_32`                                                         | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s1-v0.1.1)    | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s2-v0.1.1)    | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-all-v0.1.1)    |
-| ConvNext v2 Base | `convnextv2_base`                                                          | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s1-v0.1.1)     | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s2-v0.1.1)     | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-all-v0.1.1)     |
-| MLP-Mixer Base   | `mixer_b16_224`                                                            | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s1-v0.1.1)       | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s2-v0.1.1)       | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-all-v0.1.1)       |
-| MobileViT-S      | `mobilevit_s`                                                              | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s1-v0.1.1)         | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s2-v0.1.1)         | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-all-v0.1.1)         |
-| ResNet-50        | `resnet50`                                                                 | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s1-v0.1.1)            | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s2-v0.1.1)            | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-all-v0.1.1)            |
-| ResNet-101       | `resnet101`                                                                | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s1-v0.1.1)           | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s2-v0.1.1)           | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-all-v0.1.1)           |
-| ViT Base         | `vit_base_patch8_224`                                                      | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s1-v0.1.1) | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s2-v0.1.1) | [link](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-all-v0.1.1) |
+| 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)     |
+| MLP-Mixer Base   | `mixer_b16_224`                                                            | [MLP-Mixer Base S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s1-v0.1.1)       | [MLP-Mixer Base S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s2-v0.1.1)       | [MLP-Mixer Base S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-all-v0.1.1)       |
+| MobileViT-S      | `mobilevit_s`                                                              | [MobileViT-S S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s1-v0.1.1)         | [MobileViT-S S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s2-v0.1.1)         | [MobileViT-S S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-all-v0.1.1)         |
+| ResNet-50        | `resnet50`                                                                 | [ResNet-50 S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s1-v0.1.1)            | [ResNet-50 S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s2-v0.1.1)            | [ResNet-50 S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-all-v0.1.1)            |
+| ResNet-101       | `resnet101`                                                                | [ResNet-101 S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s1-v0.1.1)           | [ResNet-101 S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s2-v0.1.1)           | [ResNet-101 S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-all-v0.1.1)           |
+| ViT Base         | `vit_base_patch8_224`                                                      | [ViT Base S1](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s1-v0.1.1) | [ViT Base S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s2-v0.1.1) | [ViT Base S1+S2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-all-v0.1.1) |
+
 
 ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
 
 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 
-using the code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the 
-provided code.
+using the code below. Note that [`configilm`](https://pypi.org/project/configilm/) is a requirement to use the 
+code below.
 
 ```python
 from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier