TU Berlin	RSiM	DIMA	BigEarth	BIFOLD

Vit_base_patch8_224 pretrained on BigEarthNet v2.0 using Sentinel-1 & Sentinel-2 bands

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)
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: 0

The weights published in this model card were obtained after 24 training epochs. For more information, please visit the official BigEarthNet v2.0 (reBEN) repository, where you can find the training scripts.

The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results:

Metric	Macro	Micro
Average Precision	0.669646	0.840189
F1 Score	0.608862	0.741301
Precision	0.669646	0.840189

Example

A Sentinel-2 image (true color representation)

Class labels	Predicted scores
Agro-forestry areas Arable land Beaches, dunes, sands ... Urban fabric	0.140824 0.137060 0.025337 ... 0.011822

To use the model, download the codes that define the model architecture from the official BigEarthNet v2.0 (reBEN) repository and load the model using the code below. Note that you have to install configilm to use the provided code.

from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier

model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder")

e.g.

from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier

model = BigEarthNetv2_0_ImageClassifier.from_pretrained(
  "BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-all-v0.1.1")

If you use this model in your research or the provided code, please cite the following papers:

@article{clasen2024refinedbigearthnet,
  title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis}, 
  author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker},
  year={2024},
  eprint={2407.03653},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2407.03653}, 
}

@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}
}