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README.md

@@ -1,9 +1,113 @@
 ---
+thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+    - convnextv2_base
+    - BigEarthNet v2.0
+    - Remote Sensing
+    - Classification
+    - image-classification
+    - Multispectral
+library_name: configilm
+license: mit
+widget:
+  - src: example.png
+    example_title: Example
+    output:
+      - label: Agro-forestry areas
+        score: 0.028380
+      - label: Arable land
+        score: 0.569225
+      - label: Beaches, dunes, sands
+        score: 0.148004
+      - label: Broad-leaved forest
+        score: 0.005741
+      - label: Coastal wetlands
+        score: 0.003048
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+[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">
+
+# Convnextv2_base pretained on BigEarthNet v2.0 using Sentinel-1 & Sentinel-2 bands
+
+<!-- Optional images -->
+<!--
+[Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) | [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2)
+:---:|:---:
+<a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> | <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/>
+-->
+
+This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-1 & Sentinel-2 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 15 training epochs
+- Batch size: 512
+- Learning rate: 0.001
+- Dropout rate: 0.15
+- Drop Path rate: 0.15
+- 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.
+
+![[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 |
+|:------------------|------------------:|------------------:|
+| Average Precision |        0.692033 |        0.857302 |
+| F1 Score          |        0.626945 |        0.759608 |
+| Precision         | 0.751897 | 0.796884 |
+
+# Example
+|             Example Input (only RGB bands from Sentinel-2)             |
+|:---------------------------------------------------:|
+| ![[BigEarthNet](http://bigearth.net/)](example.png) |
+
+| Example Output - Labels                                                   |                                                   Example Output - Scores |
+|:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
+| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.028380 <br> 0.569225 <br> 0.148004 <br> ... <br> 0.016203 </p> |
+
+
+To use the model, download the codes that defines 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.
+
+```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/BENv2-convnextv2_base-all-v0.1.1")
+```
+
+If you use this model in your research or the provided code, 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}
+}
+```