BIFOLD-BigEarthNetv2-0
/

convnextv2_base-s2-v0.2.0

@@ -1,9 +1,109 @@
 ---
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
 ---
-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]

 ---
+thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png"
 tags:
+    - 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: 0.022634
+      - label: Arable land
+        score: 0.047579
+      - label: Beaches, dunes, sands
+        score: 0.012816
+      - label: Broad-leaved forest
+        score: 0.034033
+      - label: Coastal wetlands
+        score: 0.000944
 ---
+[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 pretrained on BigEarthNet v2.0 using 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-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)
+- 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 weights published in this model card were obtained after 14 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            |       Macro |       Micro |
+|:------------------|------------------:|------------------:|
+| Average Precision |        0.685611 |        0.852548 |
+| F1 Score          |        0.630252 |        0.757458 |
+| Precision         | 0.695574 | 0.783198 |
+# Example
+|             A Sentinel-2 image (true color representation)              |
+|:---------------------------------------------------:|
+| ![[BigEarthNet](http://bigearth.net/)](example.png) |
+| Class labels                                                              |                                                          Predicted scores |
+|:--------------------------------------------------------------------------|--------------------------------------------------------------------------:|
+| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> | <p> 0.022634 <br> 0.047579 <br> 0.012816 <br> ... <br> 0.016373 </p> |
+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/convnextv2_base-s2-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}
+}
+```