license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- arxiv:2402.03166
- artery-vein
- retinal-imaging
- segmentation
- model_hub_mixin
- pytorch_model_hub_mixin
RRWNet HRF
This repo contains the the official weights of the RRWNet model trained on the HRF dataset, from the paper "RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification", by José Morano, Guilherme Aresta, and Hrvoje Bogunović, published in Expert Systems with Applications (2024).
[arXiv] ESWA] [GitHub] [BibTeX]
RRWNet models
| Model | Dataset | Resolution | Weights |
|---|---|---|---|
| RRWNet | RITE | 720x576 (original) | Download |
| RRWNet | HRF | 1024 width (resized) | Download |
Please note that the size of the images used for training is important when using the weights for predictions.
Usage
The model can be loaded using the PyTorchModelHubMixin from the huggingface_hub package and the code from the model.py file in our repo (https://github.com/j-morano/rrwnet), which can be downloaded from here.
from huggingface_hub import PyTorchModelHubMixin
from model import RRWNet as RRWNetModel
class RRWNet(RRWNetModel, PyTorchModelHubMixin):
def __init__(self, input_ch=3, output_ch=3, base_ch=64, iterations=5):
super().__init__(input_ch, output_ch, base_ch, iterations)
model = RRWNet.from_pretrained("j-morano/rrwnet-hrf")
Preprocessing
Models are trained using enhanced images and masks.
You can preprocess the images offline using the preprocessing.py script in the repo.
The script will enhance the images and masks and save them in the specified directory.
python3 preprocessing.py --images-path data/images/ --masks-path data/masks/ --save-path data/enhanced
Citation
If you use this code, the weights, the preprocessed data, or the predictions in your research, we would greatly appreciate it if you give a star to the repo and cite our work:
@article{morano2024rrwnet,
title={RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification},
author={Morano, Jos{\'e} and Aresta, Guilherme and Bogunovi{\'c}, Hrvoje},
journal={Expert Systems with Applications},
year={2024},
doi={10.1016/j.eswa.2024.124970}
}