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README.md

@@ -29,7 +29,7 @@ widget:
 :---:|:---:|:---:|:---:|:---:
 <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">
 
-# Convnextv2_base pretained on BigEarthNet v2.0 using Sentinel-1 bands
+# Convnextv2_base pretrained on BigEarthNet v2.0 using Sentinel-1 bands
 
 <!-- Optional images -->
 <!--
@@ -40,46 +40,42 @@ widget:
 
 This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 bands. 
 It was trained using the following parameters:
-- Number of epochs: up to 100 
-  - with early stopping 
-    - after 5 epochs of no improvement 
-    - based on validation average precision (macro)
-  - the weights published in this model card were obtained after 18 training epochs
+- Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average 
+precision macro)
 - Batch size: 512
 - Learning rate: 0.001
 - Dropout rate: 0.15
 - Drop Path rate: 0.15
-- Learning rate scheduler: LinearWarmupCosineAnnealing for 1000 warmup steps
+- Learning rate scheduler: LinearWarmupCosineAnnealing for 1000  warmup steps
 - Optimizer: AdamW
 - Seed: 42
 
-The model was trained using the training script of the 
-[official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts).
-See details in this repository for more information on how to train the model given the parameters above.
+The weights published in this model card were obtained after 18 training epochs.
+For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts.
 
 ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
 
 The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:
 
-| Metric            |       Value Macro |       Value Micro |
+| Metric            |       Macro |       Micro |
 |:------------------|------------------:|------------------:|
 | Average Precision |        0.602211 |        0.789338 |
 | F1 Score          |        0.548913 |        0.696168 |
-| Precision         | 0.628933 | 0.735854 |
+| Precision         | 0.602211 | 0.789338 |
 
 # Example
-|             Example Input (VV, VH and VV/VH bands from Sentinel-1)             |
+|             A Sentinel-1 image (VV, VH and VV/VH bands are used for visualization)              |
 |:---------------------------------------------------:|
 | ![[BigEarthNet](http://bigearth.net/)](example.png) |
 
-| Example Output - Labels                                                   |                                                   Example Output - Scores |
+| Class labels                                                              |                                                          Predicted scores |
 |:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
 | <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.000005 <br> 0.000090 <br> 0.000094 <br> ... <br> 0.000058 </p> |
 
 
-To use the model, download the codes that defines the model architecture from the
+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` to use the provided code.
+code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code.
 
 ```python
 from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
@@ -93,7 +89,7 @@ e.g.
 from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
 
 model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
-  "BIFOLD-BigEarthNetv2-0/BENv2-convnextv2_base-s1-v0.1.1")
+  "BIFOLD-BigEarthNetv2-0/convnextv2_base-s1-v0.1.1")
 ```
 
 If you use this model in your research or the provided code, please cite the following papers: