Update README.md

README.md

@@ -9,6 +9,10 @@ license: mit
 short_description: Official Repository of Pretrained Models 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">
+
 # BigEarthNetv2.0 Pretrained Weights
 We provide pretrained weights for several different models. 
 All models were trained with different seeds.
@@ -16,9 +20,9 @@ The weights for the best-performing model (based on Macro Average Precision on t
 All models are available as versions using Sentinel-1 only, Sentinel-2 only or Sentinel-1 and Sentinel-2 data.
 
 The order of bands is as follows:  
-For Models using Sentinel-1 only: `["VH", "VV"]`  
-For Models using Sentinel-2 only: 10m bands, 20m bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]`  
-For Models using Sentinel-1 and Sentinel-2: 10m bands, 20m bands, S1 bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]`
+For models using Sentinel-1 only: `["VH", "VV"]`  
+For models using Sentinel-2 only: 10m bands, 20m bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]`  
+For models using Sentinel-1 and Sentinel-2: 10m bands, 20m bands, S1 bands = `["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A", "VH", "VV"]`
 
 The output classes are always in alphabetical order:  
 ['Agro-forestry areas', 'Arable land', 'Beaches, dunes, sands', 'Broad-leaved forest', 'Coastal wetlands', 
@@ -29,6 +33,8 @@ The output classes are always in alphabetical order:
 'Urban fabric']
 
 
+![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg)
+
 | 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)    |
@@ -38,3 +44,40 @@ The output classes are always in alphabetical order:
 | 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) |
+![[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.
+
+```python
+from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
+
+model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")
+```
+
+e.g.
+
+```python
+from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier
+
+model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
+  "BIFOLD-BigEarthNetv2-0/resnet50-s2-v0.1.1")
+```
+
+If you use any of these models in your research, please cite the following papers:
+```bibtex
+CITATION FOR DATASET PAPER
+```
+```bibtex
+@article{hackel2024configilm,
+  title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering},
+  author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m},
+  journal={SoftwareX},
+  volume={26},
+  pages={101731},
+  year={2024},
+  publisher={Elsevier}
+}
+```