µ-Net Weights (Keras 3)

The model weights from the paper µ-Net: ConvNext-Based U-Nets for Cosmic Muon Tomography. The model is implemented using TensorFlow and trained using 2 T4 GPUs. These weights are updated to Keras 3, the previous release was for Keras 2.

Note: these weights have been trained for 20 epochs, not 15 epochs like what is mentioned by the paper. For some reason, the Keras 3 weights perform very slightly worse than the Keras 2 weights.

For the tiny model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 3, 4, 5],
        'downward_filters': [8, 16, 32, 64, 128],
        'upward_convs': [4, 3, 2, 1],
        'upward_filters': [64, 32, 16, 8],
        'resolution': 64,
        'threshold': 1e-8
    }
)

For the base model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 4, 4, 6],
        'downward_filters': [16, 32, 64, 128, 256],
        'upward_convs': [4, 4, 2, 1],
        'upward_filters': [128, 64, 32, 16],
        'resolution': 64,
        'threshold': 1e-8
    }
)

For the large model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 4, 6, 8],
        'downward_filters': [24, 48, 96, 192, 384],
        'upward_convs': [6, 4, 2, 1],
        'upward_filters': [192, 96, 48, 24],
        'resolution': 64,
        'threshold': 1e-8
    }
)

µ-Net Weights

The model weights from the paper µ-Net: ConvNext-Based U-Nets for Cosmic Muon Tomography. The model is implemented using TensorFlow and trained using 2 T4 GPUs.

For the tiny model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 3, 4, 5],
        'downward_filters': [8, 16, 32, 64, 128],
        'upward_convs': [4, 3, 2, 1],
        'upward_filters': [64, 32, 16, 8],
        'resolution': 64,
        'threshold': 1e-8
    }
)

For the base model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 4, 4, 6],
        'downward_filters': [16, 32, 64, 128, 256],
        'upward_convs': [4, 4, 2, 1],
        'upward_filters': [128, 64, 32, 16],
        'resolution': 64,
        'threshold': 1e-8
    }
)

For the large model size,

model = Agg3D(
    **{
        'point_size': 1,
        'downward_convs': [1, 2, 4, 6, 8],
        'downward_filters': [24, 48, 96, 192, 384],
        'upward_convs': [6, 4, 2, 1],
        'upward_filters': [192, 96, 48, 24],
        'resolution': 64,
        'threshold': 1e-8
    }
)

$\mu$-Net weights

For a dosage of 1024, 2048 and 4096, use the following configuration with a point size of 3.
For a dosage of 8192 and 16384, use the following configuration with a point size of 1.

{
    'point_size': <insert-point-size-here>,
    'downward_convs': [1, 1, 2, 3, 5],
    'downward_filters': [8, 16, 64, 128, 256],
    'upward_convs': [4, 3, 2, 1],
    'upward_filters': [128, 64, 16, 8],
    'resolution': 64,
    'noise_level': 0,
    'threshold': 1e-3
}