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")
print(model.forward_encoder(torch.randn(1, 3, 224, 224), mask_ratio=0.0)[0].shape)
Citations
Model Type | Venue | Citation |
---|---|---|
BioCLIP | CVPR'24 | link |
CLIP | ICML'21 | link |
CROMA | NeurIPS'23 | link |
GeoCLAP | BMVC'23 | link |
GeoCLIP | NeurIPS'23 | link |
Presto | link | |
Prithvi | link | |
RemoteCLIP | TGRS'23 | link |
RVSA | TGRS'22 | link |
Sat2Cap | EarthVision'24 | link |
SatClip | link | |
SatMAE | NeurIPS'22 | link |
SatMAE++ | CVPR'24 | link |
ScaleMAE | ICCV'23 | link |
SINR | ICML'23 | link |
StreetCLIP | link |
List of models available here: Link
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
rshf-0.0.14.tar.gz
(2.0 MB
view hashes)
Built Distribution
rshf-0.0.14-py3-none-any.whl
(2.0 MB
view hashes)