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FPN Model Card

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Load trained model

import segmentation_models_pytorch as smp

model = smp.FPN.from_pretrained("oxford-pet-segmentation")

Model init parameters

model_init_params = {
    "encoder_name": "resnet34",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_pyramid_channels": 256,
    "decoder_segmentation_channels": 128,
    "decoder_merge_policy": "add",
    "decoder_dropout": 0.2,
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "upsampling": 4,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.9050180912017822,
        "test_dataset_iou": 0.9115252494812012
    }
]

Dataset

Dataset name: Oxford Pet

More Information

This model has been pushed to the Hub using the PytorchModelHubMixin

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Safetensors
Model size
23.2M params
Tensor type
F32
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