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Load trained model

import segmentation_models_pytorch as smp

model = smp.from_pretrained("<save-directory-or-this-repo>")

Model init parameters

model_init_params = {
    "encoder_name": "resnet34",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_use_batchnorm": True,
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_attention_type": None,
    "in_channels": 1,
    "classes": 1,
    "activation": None,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.7712932825088501,
        "test_dataset_iou": 0.7576072812080383
    }
]

Dataset

Dataset name: Breast

More Information

This model has been pushed to the Hub using the PytorchModelHubMixin

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Model size
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Inference Examples
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