| ---
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| license: cc-by-nc-4.0
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| language:
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| - en
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| tags:
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| - retinal-imaging
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| - oct
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| - foundation-model
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| - pytorch
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| - mae
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| ---
|
|
|
| # RETFound OCT — MAE checkpoint (`.pth`)
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|
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| This repository hosts **`RETFound_oct.pth`**: the full **masked autoencoder (MAE)** ViT-Large checkpoint used as the **encoder** in workflows that load RETFound with `torch.load` (custom ViT backbones, e.g. the RetinaPainter trainer). It is **not** the Hugging Face Transformers bundle (`model.safetensors` + `AutoModel`). Official RETFound code and context: [rmaphoh/RETFound_MAE](https://github.com/rmaphoh/RETFound_MAE).
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|
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| For **Transformers** / `AutoModel` / image-feature pipelines on the same research line, see [**iszt/RETFound_mae_natureOCT**](https://huggingface.co/iszt/RETFound_mae_natureOCT).
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|
|
| ## Access (gated)
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|
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| This model uses **gated user access** with **automatic approval**.
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|
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| 1. Sign in at [huggingface.co](https://huggingface.co).
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| 2. Open this model page and **accept** the access terms (contact details may be collected per Hub policy).
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| 3. Create a **read** token: [Settings → Access tokens](https://huggingface.co/settings/tokens).
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| 4. Authenticate locally: `huggingface-cli login` or set the environment variable **`HF_TOKEN`**.
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|
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| Until you complete step 2 for your account, programmatic downloads may return an access error.
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|
|
| ## Files
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|
|
| | File | Description |
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| |------|-------------|
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| | `RETFound_oct.pth` | Full MAE checkpoint (encoder + decoder weights in one file). Load with PyTorch; downstream code often keeps only the ViT encoder weights. |
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|
|
| ## Download (Python)
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|
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| ```python
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| from huggingface_hub import hf_hub_download
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|
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| path = hf_hub_download(
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| repo_id="monish563/RETFOUND",
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| filename="RETFound_oct.pth",
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| )
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| print(path)
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| ```
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|
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| Requires `huggingface_hub` and a logged-in session (or `HF_TOKEN`) after gated access is accepted.
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|
|
| ### Hugging Face CLI (alternative)
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|
|
| ```bash
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| huggingface-cli download monish563/RETFOUND RETFound_oct.pth --local-dir ~/.cache/retina_painter
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| ```
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|
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| Use the same folder layout as above if your tool expects `~/.cache/retina_painter/RETFound_oct.pth`.
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|
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| ## RetinaPainter
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|
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| In **RetinaPainter** RETFound modes, the trainer pulls this file via `trainer/src/retfound_model.py` into **`~/.cache/retina_painter/RETFound_oct.pth`**. From the RetinaPainter repo root run **`python setup_retfound.py`** (interactive) or **`python setup_retfound.py --token YOUR_HF_TOKEN`** after accepting gated access on this page.
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|
|
| ## License
|
|
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| **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)** — non-commercial use with attribution. **Commercial use** is not permitted under this license unless you have separate permission from the rights holders.
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|
|
| ## Citation
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|
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| If you use these weights or the RETFound methodology, cite the original paper:
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|
|
| > Zhou, Y., et al. A foundation model for generalizable disease detection from retinal images. *Nature* **622**, 156–163 (2023). [https://doi.org/10.1038/s41586-023-06555-x](https://doi.org/10.1038/s41586-023-06555-x)
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|
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| BibTeX:
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|
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| ```bibtex
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| @article{zhou2023foundation,
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| title={A foundation model for generalizable disease detection from retinal images},
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| author={Zhou, Yukun and Chia, Mark A and Wagner, Siegfried K and Ayhan, Murat S and Williamson, Dominic J and Struyven, Robbert R and Liu, Timing and Xu, Moucheng and Lozano, Mateo G and Woodward-Court, Peter and others},
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| journal={Nature},
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| volume={622},
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| number={7981},
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| pages={156--163},
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| year={2023},
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| publisher={Nature Publishing Group UK London}
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| }
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| ```
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|
|
| ## Disclaimer
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|
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| These weights are for **research and engineering** (e.g. feature extraction, transfer learning). They are **not** validated for clinical decision-making and must not be used as a medical device without appropriate study, oversight, and regulatory compliance where applicable.
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|
