Update README.md

README.md

@@ -9,4 +9,67 @@ license: mit
 short_description: Official Repository of Pretrained Models on BigEarthNet v2.0
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+# 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: `["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 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']
+
+
+## ConvMixer
+[ConvMixer](https://arxiv.org/abs/2201.09792https://arxiv.org/abs/2201.09792) was trained in the version with a hidden
+dimension of 768 and 32 layers (often called ConvMixer-768/32). The weights are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convmixer_768_32-all-v0.1.1)
+
+## ConvNextv2
+[ConvNext v2](https://openaccess.thecvf.com/content/CVPR2023/html/Woo_ConvNeXt_V2_Co-Designing_and_Scaling_ConvNets_With_Masked_Autoencoders_CVPR_2023_paper.html) was trained in the "base" configuration. The weights are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/convnextv2_base-all-v0.1.1)
+
+## MLP-Mixer
+[MLP-Mixer](https://proceedings.neurips.cc/paper/2021/hash/cba0a4ee5ccd02fda0fe3f9a3e7b89fe-Abstract.html) was trained in the "base" configuration with a patch size of 16. The weights are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mixer_b16_224-all-v0.1.1)
+
+## MobileViT
+[MobileViT](https://arxiv.org/abs/2110.02178) was trained in the "small" configuration. The weights are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/mobilevit_s-all-v0.1.1)
+
+## ResNet
+[ResNet](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html) was trained in the "50" and "101" configurations.
+### ResNet-50
+The weights for the "50" configuration are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet50-all-v0.1.1)
+
+### ResNet-101
+The weights for the "101" configuration are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/resnet101-all-v0.1.1)
+
+### Vision Transformer
+[ViT](https://arxiv.org/abs/2010.11929) was trained in the "base" configuration with a patch size of 8. The weights are available for the following versions:
+ - [Sentinel-1 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s1-v0.1.1)
+ - [Sentinel-2 only](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-s2-v0.1.1)
+ - [Sentinel-1 and Sentinel-2](https://huggingface.co/BIFOLD-BigEarthNetv2-0/vit_base_patch8_224-all-v0.1.1)