MAnet Model Card

Table of Contents:

• Load trained model
• Model init parameters
• Model metrics
• Dataset

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": "efficientnet-b7",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_use_norm": "batchnorm",
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_pab_channels": 64,
    "decoder_interpolation": "nearest",
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.23992781341075897,
        "test_dataset_iou": 0.18564672768115997,
        "test_per_image_accuracy": 0.9681544303894043,
        "test_dataset_accuracy": 0.9681544303894043
    }
]

Dataset

Dataset name: VIP

More Information

• Library: https://github.com/qubvel/segmentation_models.pytorch
• Docs: https://smp.readthedocs.io/en/latest/

This model has been pushed to the Hub using the PytorchModelHubMixin