brails.processors.year_built_classifier.Resnet module

class brails.processors.year_built_classifier.Resnet.ResNet(block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None)

Bases: Module

forward(x)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

brails.processors.year_built_classifier.Resnet.resnet101(pretrained=False, progress=True, **kwargs): Constructs a ResNet-101 model. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

brails.processors.year_built_classifier.Resnet.resnet152(pretrained=False, progress=True, **kwargs): Constructs a ResNet-152 model. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

brails.processors.year_built_classifier.Resnet.resnet18(pretrained=False, progress=True, **kwargs): Constructs a ResNet-18 model. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

brails.processors.year_built_classifier.Resnet.resnet34(pretrained=False, progress=True, **kwargs): Constructs a ResNet-34 model. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

brails.processors.year_built_classifier.Resnet.resnet50(pretrained=False, progress=True, **kwargs): Constructs a ResNet-50 model. :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

brails.processors.year_built_classifier.Resnet.resnext101_32x8d(**kwargs)

brails.processors.year_built_classifier.Resnet.resnext50_32x4d(**kwargs)