rshf

Remote sensing pretrained models easy loading using huggingface -- PyTorch (for fast benchmarking)

Installation:

pip install rshf

Example:

from rshf.satmae import SatMAE
model = SatMAE.from_pretrained("MVRL/satmae-vitlarge-fmow-pretrain-800")
input = model.transform(torch.randint(0, 256, (224, 224, 3)).float().numpy(), 224).unsqueeze(0)
print(model.forward_encoder(input, mask_ratio=0.0)[0].shape)

TODO:

• [ ] Add transforms for each model
• [ ] Add Documentation (https://rshf-docs.readthedocs.io/en/latest/)
• [x] Add initial set of models

Citations

|Model Type|Venue|Citation| |----------|-----|--------| |BioCLIP|CVPR'24|link |Climplicit|ICLRW'25|link |CLIP|ICML'21|link |CROMA|NeurIPS'23|link |GeoCLAP|BMVC'23|link |GeoCLIP|NeurIPS'23|link |Presto||link |Prithvi||link |RCME|ICCV'25|link |RemoteCLIP|TGRS'23|link |RVSA|TGRS'22|link |Sat2Cap|EarthVision'24|link |SatClip|AAAI'25|link |SatMAE|NeurIPS'22|link |SatMAE++|CVPR'24|link |ScaleMAE|ICCV'23|link |SenCLIP|WACV'25|link |SINR|ICML'23|link |StreetCLIP||link |TaxaBind|WACV'25|link

List of models available here: Link