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OCULARNet

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

Sourcehuggingface.co/Anon-User-Retina/OCULARNet/tree/63cc6b80f2ee2eceb39e3fe99d1c49cea7cec927
KindCheckpointVersionRevision 63cc6b8Publisher@gplazaarriolaCgrade

Model source

Kind
Checkpoint, Segmentation model, UNet
File size
1.17 GB
Version
vRevision 63cc6b8
Updated
Oct 3, 2026
Publisher
@gplazaarriola
Downloads
0
Tasks
Image segmentation
Source
Hugging Face

Source description

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)

Sources

1 source
Hugging FacePrimaryVersion 63cc6b80f2ee2eceb39e3fe99d1c49cea7cec927
huggingface.co/Anon-User-Retina/OCULARNet/tree/63cc6b80f2ee2eceb39e3fe99d1c49cea7cec927

Verified Oct 3

Model artifacts

1 artifact

OCULARNet.pth

pth · 1.17 GB · SHA-256 88eec1d78ec9…df75 · Hugging Face

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Source excerpts

3 excerpts
ReadmeJul 11huggingface.co/Anon-User-Retina/OCULARNet

--- license: mit pipeline_tag: image-segmentation --- # 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](`https://github.com/GonzaloPlaaza/OCULAR`). ## Usage ```python 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)

NoteJul 11huggingface.co/Anon-User-Retina/OCULARNet

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo Anon-User-Retina/OCULARNet

NoteJun 9huggingface.co/api/models/gplazaarriola/OCULARNet

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo gplazaarriola/OCULARNet