Keras documentation: ResNet and ResNetV2 (2024)

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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

• Deep Residual Learning for Image Recognition (CVPR 2015)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet.preprocess_input on yourinputs before passing them to the model. resnet.preprocess_input will convertthe input images from RGB to BGR, then will zero-center each color channel withrespect 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.

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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

• Deep Residual Learning for Image Recognition (CVPR 2015)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet.preprocess_input on yourinputs before passing them to the model. resnet.preprocess_input will convertthe input images from RGB to BGR, then will zero-center each color channel withrespect 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

• Deep Residual Learning for Image Recognition (CVPR 2015)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet.preprocess_input on yourinputs before passing them to the model. resnet.preprocess_input will convertthe input images from RGB to BGR, then will zero-center each color channel withrespect 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.

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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

• Identity Mappings in Deep Residual Networks (CVPR 2016)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet_v2.preprocess_input on yourinputs before passing them to the model. resnet_v2.preprocess_input willscale 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

• Identity Mappings in Deep Residual Networks (CVPR 2016)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet_v2.preprocess_input on yourinputs before passing them to the model. resnet_v2.preprocess_input willscale 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

• Identity Mappings in Deep Residual Networks (CVPR 2016)

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

For transfer learning use cases, make sure to read the.

Note: each Keras Application expects a specific kind of input preprocessing.For ResNet, call keras.applications.resnet_v2.preprocess_input on yourinputs before passing them to the model. resnet_v2.preprocess_input willscale 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.