larq_zoo.literature

BinaryAlexNet

BinaryAlexNet(input_shape=None,
              input_tensor=None,
              weights='imagenet',
              include_top=True,
              num_classes=1000)
Instantiates the BinaryAlexNet architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

BiRealNet

BiRealNet(input_shape=None,
          input_tensor=None,
          weights='imagenet',
          include_top=True,
          num_classes=1000)
Instantiates the Bi-Real Net architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BinaryResNetE18

BinaryResNetE18(input_shape=None,
                input_tensor=None,
                weights='imagenet',
                include_top=True,
                num_classes=1000)
Instantiates the BinaryResNetE 18 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BinaryDenseNet28

BinaryDenseNet28(input_shape=None,
                 input_tensor=None,
                 weights='imagenet',
                 include_top=True,
                 num_classes=1000)
Instantiates the BinaryDenseNet 28 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BinaryDenseNet37

BinaryDenseNet37(input_shape=None,
                 input_tensor=None,
                 weights='imagenet',
                 include_top=True,
                 num_classes=1000)
Instantiates the BinaryDenseNet 37 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BinaryDenseNet37Dilated

BinaryDenseNet37Dilated(input_shape=None,
                        input_tensor=None,
                        weights='imagenet',
                        include_top=True,
                        num_classes=1000)
Instantiates the BinaryDenseNet 37Dilated architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BinaryDenseNet45

BinaryDenseNet45(input_shape=None,
                 input_tensor=None,
                 weights='imagenet',
                 include_top=True,
                 num_classes=1000)
Instantiates the BinaryDenseNet 45 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

DoReFaNet

DoReFaNet(input_shape=None,
          input_tensor=None,
          weights='imagenet',
          include_top=True,
          num_classes=1000)
Instantiates the DoReFa-net architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

XNORNet

XNORNet(input_shape=None,
        input_tensor=None,
        weights='imagenet',
        include_top=True,
        num_classes=1000)
Instantiates the XNOR-Net architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

Arguments

  • input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • include_top: whether to include the fully-connected layer at the top of the network.
  • num_classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References