Official Hugging Face Transformers-format releases of SARMAE ViT encoders, converted for native transformers inference with trust_remote_code=True.

Resource	Link
Paper	2512.16635
Training dataset	Wenquandan777/SAR-1M
Legacy PyTorch weights	Wenquandan777/SARMAE
Source code	GitHub

Available checkpoints

Variant	Backbone	Stage	Hidden	Layers	Heads	Input
sarmae-vit-base-patch16-pretrain	ViT-B	pretrain	768	12	12	224
sarmae-vit-large-patch16-pretrain	ViT-L	pretrain	1024	24	16	224

Installation

pip install transformers timm torch torchvision safetensors huggingface_hub

Usage

Point transformers.pipeline or AutoModel.from_pretrained at a variant subfolder:

python

from transformers import pipeline

pipe = pipeline(
    task="image-feature-extraction",
    model="BiliSakura/SARMAE-transformers",
    revision="main",
    trust_remote_code=True,
    model_kwargs={"subfolder": "vit-base-patch16-pretrain"},
)
features = pipe(sar_image, pool=True, return_tensors=True)

Or load a variant directly:

python

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "BiliSakura/SARMAE-transformers",
    subfolder="vit-base-patch16-pretrain",
    trust_remote_code=True,
)

Each variant folder is a self-contained model repository with:

config.json (auto_map, custom_pipelines)
model.safetensors
preprocessor_config.json
modeling_sarmae.py, image_processing_sarmae.py, pipeline_sarmae.py

Convert legacy checkpoints locally

bash

python scripts/convert_checkpoint.py models/SARMAE_vitb_checkpoint-last
python scripts/convert_checkpoint.py models/SARMAE_vitl_checkpoint-last

Upload to this Hub repo

bash

python scripts/upload_to_hub.py models/sarmae-vit-base-patch16-pretrain \
  --path-in-repo vit-base-patch16-pretrain
python scripts/upload_to_hub.py models/sarmae-vit-large-patch16-pretrain \
  --path-in-repo vit-large-patch16-pretrain
python scripts/upload_to_hub.py --hub-readme-only

Citation

bibtex

@misc{liu2025sarmaemaskedautoencodersar,
  title={SARMAE: Masked Autoencoder for SAR Representation Learning},
  author={Danxu Liu and Di Wang and Hebaixu Wang and Haoyang Chen and Wentao Jiang and Yilin Cheng and Haonan Guo and Wei Cui and Jing Zhang},
  year={2025},
  eprint={2512.16635},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2512.16635},
}

License

CC BY-NC 4.0