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| license: mit |
| pipeline_tag: image-segmentation |
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| # Retinal Artery-Vein Segmentation Models |
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| ## Available checkpoints |
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| 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. |
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| Datasets, training and inference details can be found in our [GitHub repository](`https://github.com/GonzaloPlaaza/OCULAR`). |
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| ## Usage |
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| ```python |
| import torch |
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| device = torch.device('cuda:0' if use_cuda else 'cpu') |
| checkpoint = torch.load("OCULARNet.pth", map_location=device) |
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| if "model_state_dict" in checkpoint: |
| state_dict = checkpoint["model_state_dict"] |
| else: |
| state_dict = checkpoint |
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| model.load_state_dict(state_dict) |
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