RS pretrained models in huggingface style
Project description
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 |
| DinoV3 Sat | link | |
| GeoCLAP | BMVC'23 | link |
| GeoCLIP | NeurIPS'23 | link |
| Presto | link | |
| Prithvi | link | |
| ProM3E | CVPR'26 | link |
| RANGE | CVPR'25 | 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
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