gplazaarriola/OCULARNet

Retinal Artery-Vein Segmentation Models

Available checkpoints

We present the OCULARNet model, a base UNet encoder - (256, 128, 64, 32) layer size - coupled with a RepVGG-B3 decoder loaded from segmentation_models_pytorch for retinal artery-vein segmentation.

Datasets, training and inference details can be found in our GitHub repository.

Usage

import torch

device = torch.device('cuda:0' if use_cuda else 'cpu')
checkpoint = torch.load("OCULARNet.pth", map_location=device)
        
if "model_state_dict" in checkpoint:
    state_dict = checkpoint["model_state_dict"]
else:
    state_dict = checkpoint

model.load_state_dict(state_dict)