metadata

title: README
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: static
pinned: false
license: mit
short_description: Official Repository of Pretrained Models on BigEarthNet v2.0

TU Berlin	RSiM	DIMA	BigEarth	BIFOLD

BigEarthNetv2.0 Pretrained Weights

We provide pretrained weights for several different models. All models were trained with different seeds. The weights for the best-performing model (based on Macro Average Precision on the recommended test split) are uploaded. 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: Sentinel-1 bands ["VH", "VV"]
For models using Sentinel-2 only: Sentinel-2 10m bands and 20m bands ["B02", "B03", "B04", "B08", "B05", "B06", "B07", "B11", "B12", "B8A"]
For models using Sentinel-1 and Sentinel-2: Sentinel-2 10m bands and 20m bands and Sentinel-1 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', 'Complex cultivation patterns', 'Coniferous forest', 'Industrial or commercial units', 'Inland waters', 'Inland wetlands', 'Land principally occupied by agriculture, with significant areas of natural vegetation', 'Marine waters', 'Mixed forest', 'Moors, heathland and sclerophyllous vegetation', 'Natural grassland and sparsely vegetated areas', 'Pastures', 'Permanent crops', 'Transitional woodland, shrub', 'Urban fabric']

Model	equivalent `timm` model name	Sentinel-1 only	Sentinel-2 only	Sentinel-1 and Sentinel-2
ConvMixer-768/32	`convmixer_768_32`	link	link	link
ConvNext v2 Base	`convnextv2_base`	link	link	link
MLP-Mixer Base	`mixer_b16_224`	link	link	link
MobileViT-S	`mobilevit_s`	link	link	link
ResNet-50	`resnet50`	link	link	link
ResNet-101	`resnet101`	link	link	link
ViT Base	`vit_base_patch8_224`	link	link	link

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/resnet50-s2-v0.1.1")

If you use any of these models in your research, please cite the following papers:

CITATION FOR DATASET PAPER

@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}
}