Powerful Archaeological Segmentation Service for Erasing Ramps Enabled by Learning (PASSEREL)

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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": "resnext50_32x4d",
    "encoder_depth": 5,
    "encoder_weights": "swsl",
    "decoder_use_norm": "batchnorm",
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_attention_type": None,
    "decoder_interpolation": "nearest",
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "aux_params": None
}

but

"activation": sigmoide

makes a better result if you use the result not for metrics.

Model metrics

[More Information Needed]

Dataset

Dataset name: [More Information Needed]

More Information

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

This model has been pushed to the Hub using the PytorchModelHubMixin