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/)
• 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