AlbUNet

This module created AlbUNet: U-Net with ResNet encoder. This model writed by Alexander Buslaev and spoiled by me.

This model can be constructed with ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’, ‘resnet152’ encoders.

For create model just call resnet<number> method

neural_pipeline.builtin.models.albunet.resnet18(classes_num: int, in_channels: int, pretrained: bool = True)

Constructs a AlbUNet with ResNet-18 encoder.

Parameters:

• classes_num – number of classes (number of masks in output)
• in_channels – number of input channels
• pretrained – If True, returns a model with encoder pre-trained on ImageNet

neural_pipeline.builtin.models.albunet.resnet34(classes_num: int, in_channels: int, pretrained: bool = True)

Constructs a AlbUNet with ResNet-34 encoder.

Parameters:

• classes_num – number of classes (number of masks in output)
• in_channels – number of input channels
• pretrained – If True, returns a model with encoder pre-trained on ImageNet

neural_pipeline.builtin.models.albunet.resnet50(classes_num: int, in_channels: int, pretrained: bool = True)

Constructs a AlbUNet with ResNet-50 encoder.

Parameters:

• classes_num – number of classes (number of masks in output)
• in_channels – number of input channels
• pretrained – If True, returns a model with encoder pre-trained on ImageNet

neural_pipeline.builtin.models.albunet.resnet101(classes_num: int, in_channels: int, pretrained: bool = True)

Constructs a AlbUNet with ResNet-101 encoder.

Parameters:

• classes_num – number of classes (number of masks in output)
• in_channels – number of input channels
• pretrained – If True, returns a model with encoder pre-trained on ImageNet

neural_pipeline.builtin.models.albunet.resnet152(classes_num: int, in_channels: int, pretrained: bool = True)

Constructs a AlbUNet with ResNet-152 encoder.

Parameters:

• classes_num – number of classes (number of masks in output)
• in_channels – number of input channels
• pretrained – If True, returns a model with encoder pre-trained on ImageNet