ModelVérifiéExterne

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

TypeCheckpointVersionRevision 63cc6b8Éditeur@gplazaarriolaCClasse

Source du modèle

Type: Checkpoint, Segmentation model, UNet
Taille du fichier: 1,17 GB
Version: vRevision 63cc6b8
Mis à jour: 3 oct. 2026
Éditeur: @gplazaarriola
Téléchargements: 0
Tâches: Segmentation d'image
Source: Hugging Face

Description de la source

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 FacePrincipalVersion 63cc6b80f2ee2eceb39e3fe99d1c49cea7cec927

huggingface.co/Anon-User-Retina/OCULARNet/tree/63cc6b80f2ee2eceb39e3fe99d1c49cea7cec927

Vérifié 3 oct.

Artefacts du modèle

1 artefact

OCULARNet.pth

pth · 1,17 GB · SHA-256 88eec1d78ec9…df75 · Hugging Face

Télécharger

Extraits de sources

3 extraits

Readme11 juil.huggingface.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)

Note11 juil.huggingface.co/Anon-User-Retina/OCULARNet

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

Note9 juinhuggingface.co/api/models/gplazaarriola/OCULARNet

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