BiliSakura / SARMAE-transformers

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Model's Last Updated: August 09 2026
image-feature-extraction

Introduction of SARMAE-transformers

Model Details of SARMAE-transformers

SARMAE Transformers Checkpoints

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:

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:

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
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
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
@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

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SARMAE-transformers huggingface.co

SARMAE-transformers huggingface.co is an AI model on huggingface.co that provides SARMAE-transformers's model effect (), which can be used instantly with this BiliSakura SARMAE-transformers model. huggingface.co supports a free trial of the SARMAE-transformers model, and also provides paid use of the SARMAE-transformers. Support call SARMAE-transformers model through api, including Node.js, Python, http.

SARMAE-transformers huggingface.co Url

https://huggingface.co/BiliSakura/SARMAE-transformers

BiliSakura SARMAE-transformers online free

SARMAE-transformers huggingface.co is an online trial and call api platform, which integrates SARMAE-transformers's modeling effects, including api services, and provides a free online trial of SARMAE-transformers, you can try SARMAE-transformers online for free by clicking the link below.

BiliSakura SARMAE-transformers online free url in huggingface.co:

https://huggingface.co/BiliSakura/SARMAE-transformers

SARMAE-transformers install

SARMAE-transformers is an open source model from GitHub that offers a free installation service, and any user can find SARMAE-transformers on GitHub to install. At the same time, huggingface.co provides the effect of SARMAE-transformers install, users can directly use SARMAE-transformers installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

SARMAE-transformers install url in huggingface.co:

https://huggingface.co/BiliSakura/SARMAE-transformers

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SARMAE-transformers huggingface.co Url

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