## RETFound - A foundation model for retinal imaging
Official repo including a series of retinal foundation models.
[RETFound: a foundation model for generalizable disease detection from retinal images](https://www.nature.com/articles/s41586-023-06555-x), which is based on [MAE](https://github.com/facebookresearch/mae).
[New checkpoints](https://huggingface.co/YukunZhou), some of which are based on [DINOV2](https://github.com/facebookresearch/dinov2):
Please contact **ykzhoua@gmail.com** or **yukun.zhou.19@ucl.ac.uk** if you have questions.
Keras version implemented by Yuka Kihara can be found [here](https://github.com/uw-biomedical-ml/RETFound_MAE)
### 📝Key features
- RETFound is pre-trained on 1.6 million retinal images with self-supervised learning
- RETFound has been validated in multiple disease detection tasks
- RETFound can be efficiently adapted to customised tasks
### 🎉News
- 🐉2025/02: **We organised the model weights on HuggingFace, no more manual downloads needed!**
- 🐉2025/02: **Multiple [pre-trained weights](https://huggingface.co/YukunZhou), including MAE-based and DINOV2-based, are added!**
- 🐉2025/02: **We update the version of packages, such as CUDA12+ and PyTorch 2.3+!**
- 🐉2024/01: [Feature vector notebook](https://github.com/rmaphoh/RETFound_MAE/blob/main/latent_feature.ipynb) are now online!
- 🐉2024/01: [Data split and model checkpoints](BENCHMARK.md) for public datasets are now online!
- 🎄2023/12: [Colab notebook](https://colab.research.google.com/drive/1_X19zdMegmAlqPAEY0Ao659fzzzlx2IZ?usp=sharing) is now online - free GPU & simple operation!
- 2023/10: change the hyperparameter of [input_size](https://github.com/rmaphoh/RETFound_MAE#:~:text=finetune%20./RETFound_cfp_weights.pth%20%5C-,%2D%2Dinput_size%20224,-For%20evaluation%20only) for any image size
### 🔧Install environment
1. Create environment with conda:
```
conda create -n retfound python=3.11.0 -y
conda activate retfound
```
2. Install dependencies
```
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
git clone https://github.com/rmaphoh/RETFound_MAE/
cd RETFound_MAE
pip install -r requirements.txt
```
### 🌱Fine-tuning with RETFound weights
To fine tune RETFound on your own data, follow these steps:
1. Get access to the pre-trained models on HuggingFace (register an account and fill in the form) and go to step 2:
2. Login in your HuggingFace account, where HuggingFace token can be [created and copied](https://huggingface.co/settings/tokens).
```
huggingface-cli login --token YOUR_HUGGINGFACE_TOKEN
```
**Optional**: if your machine and server cannot access HuggingFace due to internet wall, run the command below (Do not run it if you can access):
```
export HF_ENDPOINT=https://hf-mirror.com
```
3. Organise your data into this directory structure (Public datasets used in this study can be [downloaded here](BENCHMARK.md))
```
├── data folder
├──train
├──class_a
├──class_b
├──class_c
├──val
├──class_a
├──class_b
├──class_c
├──test
├──class_a
├──class_b
├──class_c
```
4. Start fine-tuning (use IDRiD as example). A fine-tuned checkpoint will be saved during training. Evaluation will be automatically run after training.
The model and finetune can be selected:
| model | finetune |
|-----------------|--------------------------|
| RETFound_mae | RETFound_mae_natureCFP |
| RETFound_mae | RETFound_mae_natureOCT |
| RETFound_mae | RETFound_mae_meh |
| RETFound_mae | RETFound_mae_shanghai |
| RETFound_dinov2 | RETFound_dinov2_meh |
| RETFound_dinov2 | RETFound_dinov2_shanghai |
```
torchrun --nproc_per_node=1 --master_port=48798 main_finetune.py \
--model RETFound_mae \
--savemodel \
--global_pool \
--batch_size 16 \
--world_size 1 \
--epochs 100 \
--blr 5e-3 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.2 \
--nb_classes 5 \
--data_path ./IDRiD \
--input_size 224 \
--task RETFound_mae_meh-IDRiD \
--finetune RETFound_mae_meh
```
4. For evaluation only (download data and model checkpoints [here](BENCHMARK.md); change the path below)
```
torchrun --nproc_per_node=1 --master_port=48798 main_finetune.py \
--model RETFound_mae \
--savemodel \
--eval \
--global_pool \
--batch_size 16 \
--world_size 1 \
--epochs 100 \
--blr 5e-3 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.2 \
--nb_classes 5 \
--data_path ./IDRiD \
--input_size 224 \
--task RETFound_mae_meh-IDRiD \
--resume ./RETFound_mae_meh-IDRiD/checkpoint-best.pth
```
### 📃Citation
If you find this repository useful, please consider citing this paper:
```
TBD
```
```
@article{zhou2023foundation,
title={A foundation model for generalizable disease detection from retinal images},
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},
journal={Nature},
volume={622},
number={7981},
pages={156--163},
year={2023},
publisher={Nature Publishing Group UK London}
}
```