## RETFound - A foundation model for retinal imaging
Official repo including a series of foundation models and applications in retinal imaging.
`[RETFound-MAE]`:[RETFound: a foundation model for generalizable disease detection from retinal images](https://www.nature.com/articles/s41586-023-06555-x).
`[RETFound-DINOv2]`:[Revealing the Impact of Pre-training Data on Medical Foundation Models](https://www.researchsquare.com/article/rs-6080254/v1).
`[DINOv2]`:[General-purpose vision foundation models DINOv2](https://github.com/facebookresearch/dinov2).
`[DINOv3]`:[General-purpose vision foundation models DINOv3](https://github.com/facebookresearch/dinov3).
Please contact **ykzhoua@gmail.com** or **yukun.zhou.19@ucl.ac.uk** if you have questions.
### 📝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
- 🐉2025/09: **Benchmarking paper for DINOv3, DINOv2, and RETFound will come soon!**
- 🐉2025/09: **We included state-of-the-art DINOv3 into fine-tuning pipeline for retinal applications!**
- 🐉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!
### 🔧Install environment
1. Create environment with conda:
```
conda create -n retfound python=3.11.0 -y
conda activate retfound
```
2. Install dependencies
```
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121
git clone https://github.com/rmaphoh/RETFound/
cd RETFound
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. If you would like to use DINOv2 and DINOv3, please visit their GitHub repositories to download the model weights and put them in the RETFound folder.
4. Start fine-tuning by running `sh train.sh`.
The model can be selected by changing the hyperparameters `MODEL`, `MODEL_ARCH`, `FINETUNE` in `train.sh`:
**RETFound**:
| MODEL | MODEL_ARCH | FINETUNE | SIZE |
|-----------------|--------------------------|--------------------------|--------------------------|
| RETFound_mae | retfound_mae | RETFound_mae_natureCFP | ~300M |
| RETFound_mae | retfound_mae | RETFound_mae_natureOCT | ~300M |
| RETFound_mae | retfound_mae | RETFound_mae_meh | ~300M |
| RETFound_mae | retfound_mae | RETFound_mae_shanghai | ~300M |
| RETFound_dinov2 | retfound_dinov2 | RETFound_dinov2_meh | ~300M |
| RETFound_dinov2 | retfound_dinov2 | RETFound_dinov2_shanghai | ~300M |
**DINOv3**:
| MODEL | MODEL_ARCH | FINETUNE | SIZE |
|-----------------|--------------------------|----------------------------------|--------------------------|
| Dinov3 | dinov3_vits16 | dinov3_vits16_pretrain.pth | ~21M |
| Dinov3 | dinov3_vits16plus | dinov3_vits16plus_pretrain.pth | ~29M |
| Dinov3 | dinov3_vitb16 | dinov3_vitb16_pretrain.pth | ~86M |
| Dinov3 | dinov3_vitl16 | dinov3_vitl16_pretrain.pth | ~300M |
| Dinov3 | dinov3_vith16plus | dinov3_vith16plus_pretrain.pth | ~840M |
| Dinov3 | dinov3_vit7b16 | dinov3_vit7b16_pretrain.pth | ~6.7B |
**DINOv2**:
| MODEL | MODEL_ARCH | FINETUNE | SIZE |
|-----------------|--------------------------|------------------------------|--------------------------|
| Dinov2 | dinov2_vits14 | dinov2_vits14_pretrain.pth | ~21M |
| Dinov2 | dinov2_vitb14 | dinov2_vitb14_pretrain.pth | ~86M |
| Dinov2 | dinov2_vitl14 | dinov2_vitl14_pretrain.pth | ~300M |
| Dinov2 | dinov2_vitg14 | dinov2_vitg14_pretrain.pth | ~1.1B |
```
# ==== Model settings ====
# adaptation {finetune,lp}
ADAPTATION="finetune"
MODEL="RETFound_dinov2"
MODEL_ARCH="retfound_dinov2"
FINETUNE="RETFound_dinov2_meh"
# ==== Data settings ====
# change the dataset name and corresponding class number
DATASET="MESSIDOR2"
NUM_CLASS=5
data_path="./${DATASET}"
task="${MODEL_ARCH}_${DATASET}_${ADAPTATION}"
torchrun --nproc_per_node=1 --master_port=48766 main_finetune.py \
--model "${MODEL}" \
--model_arch "${MODEL_ARCH}" \
--finetune "${FINETUNE}" \
--savemodel \
--global_pool \
--batch_size 24 \
--world_size 1 \
--epochs 50 \
--nb_classes "${NUM_CLASS}" \
--data_path "${data_path}" \
--input_size 224 \
--task "${task}" \
--adaptation "${ADAPTATION}"
```
4. For evaluation only (download data and model checkpoints [here](BENCHMARK.md); change the path below)
```
# ==== Model/settings (match training) ====
ADAPTATION="finetune"
MODEL="RETFound_dinov2"
MODEL_ARCH="retfound_dinov2"
FINETUNE="RETFound_dinov2_meh"
# ==== Data/settings (match training) ====
DATASET="MESSIDOR2"
NUM_CLASS=5
DATA_PATH="./${DATASET}"
TASK="${MODEL_ARCH}_${DATASET}_${ADAPTATION}"
# Path to the trained checkpoint (adjust if you saved elsewhere)
CKPT="./output_dir/${TASK}/checkpoint-best.pth"
# ==== Evaluation only ====
torchrun --nproc_per_node=1 --master_port=48766 main_finetune.py \
--model "${MODEL}" \
--model_arch "${MODEL_ARCH}" \
--savemodel \
--global_pool \
--batch_size 128 \
--world_size 1 \
--nb_classes "${NUM_CLASS}" \
--data_path "${DATA_PATH}" \
--input_size 224 \
--task "${TASK}" \
--adaptation "${ADAPTATION}" \
--eval \
--resume "${CKPT}"
```
### 📃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}
}
```