A Foundation LAnguage Image model of the Retina (FLAIR):
Encoding expert knowledge in text supervision
Julio Silva-Rodriguez1,
Hadi Chakor2,
Riadh Kobbi2,
Jose Dolz1,
Ismail Ben Ayed1
1LIVIA - ETS Montreal, 2DIAGNOS Inc.
| Project | Paper | Code | Tutorials |
- Install in your enviroment a compatible torch version with your GPU. For example:
conda create -n flair_env python=3.8 -y
conda activate flair_env
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
- Install FLAIR library.
pip install git+https://github.com/jusiro/FLAIR.git
from PIL import Image
import numpy as np
# Import FLAIR
from flair import FLAIRModel
# Set model
model = FLAIRModel(from_checkpoint=True)
# Load image and set target categories
# (if the repo is not cloned, download the image and change the path!)
image = np.array(Image.open("./documents/sample_macular_hole.png"))
text = ["normal", "healthy", "macular edema", "diabetic retinopathy", "glaucoma", "macular hole",
"lesion", "lesion in the macula"]
# Forward FLAIR model to compute similarities
probs, logits = model(image, text)
print("Image-Text similarities:")
print(logits.round(3)) # [[-0.32 -2.782 3.164 4.388 5.919 6.639 6.579 10.478]]
print("Probabilities:")
print(probs.round(3)) # [[0. 0. 0.001 0.002 0.01 0.02 0.019 0.948]]
If you encounter any issue while downloading the pre-trained weights (i.e. from_checkpoint=True), you can manually download the weights from the following links (see Table), unzip the file, and store them at: ./flair/modeling/flair_pretrained_weights/[ID].pth.
| Backbone | ID | |
|---|---|---|
| ResNet-50 | flair_resnet | LINK |
In the following, we present the scripts for model pre-training and transferability. To use them, we recommend cloning the whole repository.
git clone https://github.com/jusiro/FLAIR.git
cd FLAIR
pip install -r requirements.txt
-
Define the relative paths for datasets and dataframes in
./local_data/constants.py. -
Prepare the FUNDUS assembly dataset - check
./local_data/prepare_partitions.pyto prepare the dataframes.
- Vision-Language Pre-training.
python main_pretrain.py --augment_description True --balance True --epochs 15 --batch_size 128 --num_workers 6
- Define the relative paths for datasets and dataframes in
./local_data/constants.py. - Prepare the experiment setting for the target dataset - we used
./local_data/experiments.pyto store them.
if experiment == "02_MESSIDOR":
setting = {"dataframe": PATH_DATAFRAME_TRANSFERABILITY_CLASSIFICATION + "02_MESSIDOR.csv",
"task": "classification",
"targets": {"no diabetic retinopathy": 0,
"mild diabetic retinopathy": 1,
"moderate diabetic retinopathy": 2,
"severe diabetic retinopathy": 3,
"proliferative diabetic retinopathy": 4}}
- Zero-shot (no adaptation).
python main_transferability.py --experiment 02_MESSIDOR --method zero_shot --load_weights True --domain_knowledge True --shots_train 0% --shots_test 100% --project_features True --norm_features True --folds 1
- Linear Probing.
python main_transferability.py --experiment 02_MESSIDOR --method lp --load_weights True --shots_train 80% --shots_test 20% --project_features False --norm_features False --folds 5
If you find this repository useful, please consider citing this paper:
@article{FLAIR2023,
title={A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision},
author={Julio Silva-Rodriguez and Hadi Chakor and Riadh Kobbi and Jose Dolz and Ismail Ben Ayed},
journal={ArXiv Preprint},
year={2023}
}
- Code and Model Weights are released under Apache 2.0 license