Add SARMAE transformers Hub model card

README.md

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+---
+license: cc-by-nc-4.0
+language: en
+tags:
+  - vision
+  - image-feature-extraction
+  - sar
+  - remote-sensing
+  - synthetic-aperture-radar
+  - masked-autoencoder
+  - model-hub
+library_name: transformers
+pipeline_tag: image-feature-extraction
+datasets:
+  - Wenquandan777/SAR-1M
+arxiv: 2512.16635
+---
+
+# SARMAE Transformers Checkpoints
+
+Official Hugging Face Transformers-format releases of [SARMAE](https://arxiv.org/abs/2512.16635) ViT encoders, converted for native `transformers` inference with `trust_remote_code=True`.
+
+| Resource | Link |
+|----------|------|
+| Paper | [2512.16635](https://arxiv.org/abs/2512.16635) |
+| Training dataset | [Wenquandan777/SAR-1M](https://huggingface.co/datasets/Wenquandan777/SAR-1M) |
+| Legacy PyTorch weights | [Wenquandan777/SARMAE](https://huggingface.co/Wenquandan777/SARMAE) |
+| Source code | [GitHub](https://github.com/BiliSakura/SARMAE-transformers) |
+
+## Available checkpoints
+
+| Variant | Backbone | Stage | Hidden | Layers | Heads | Input |
+|---------|----------|-------|--------|--------|-------|-------|
+| [sarmae-vit-base-patch16-pretrain](https://huggingface.co/BiliSakura/SARMAE-transformers/tree/main/vit-base-patch16-pretrain) | ViT-B | pretrain | 768 | 12 | 12 | 224 |
+| [sarmae-vit-large-patch16-pretrain](https://huggingface.co/BiliSakura/SARMAE-transformers/tree/main/vit-large-patch16-pretrain) | ViT-L | pretrain | 1024 | 24 | 16 | 224 |
+
+## Installation
+
+```bash
+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](https://creativecommons.org/licenses/by-nc/4.0/)