Keras 3 API documentation / Keras Applications / ResNet and ResNetV2

ResNet and ResNetV2

[source]

ResNet50 function

keras.applications.ResNet50(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet50 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.


[source]

ResNet101 function

keras.applications.ResNet101(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet101 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.


[source]

ResNet152 function

keras.applications.ResNet152(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet152 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet.preprocess_input on your inputs before passing them to the model. resnet.preprocess_input will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.


[source]

ResNet50V2 function

keras.applications.ResNet50V2(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet50V2 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.preprocess_input will scale input pixels between -1 and 1.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.


[source]

ResNet101V2 function

keras.applications.ResNet101V2(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet101V2 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.preprocess_input will scale input pixels between -1 and 1.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.


[source]

ResNet152V2 function

keras.applications.ResNet152V2(
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
)

Instantiates the ResNet152V2 architecture.

Reference

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For ResNet, call keras.applications.resnet_v2.preprocess_input on your inputs before passing them to the model. resnet_v2.preprocess_input will scale input pixels between -1 and 1.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value.
  • pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - max means that global max pooling will be applied.
  • 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.
  • classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A Model instance.