Applications

Keras Applications are deep learning models that are made available alongside pre-trained weights.These models can be used for prediction, feature extraction, and fine-tuning.

Weights are downloaded automatically when instantiating a model. They are stored at ~/.keras/models/.

Available models

Models for image classification with weights trained on ImageNet:

• Xception
• VGG16
• VGG19
• ResNet, ResNetV2
• InceptionV3
• InceptionResNetV2
• MobileNet
• MobileNetV2
• DenseNet
• NASNet

All of these architectures are compatible with all the backends (TensorFlow, Theano, and CNTK), and upon instantiation the models will be built according to the image data format set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_data_format=channels_last, then any model loaded from this repository will get built according to the TensorFlow data format convention, "Height-Width-Depth".

Note that:- For Keras < 2.2.0, The Xception model is only available for TensorFlow, due to its reliance on SeparableConvolution layers.- For Keras < 2.1.5, The MobileNet model is only available for TensorFlow, due to its reliance on DepthwiseConvolution layers.

Usage examples for image classification models

Classify ImageNet classes with ResNet50

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet')
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])
# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]

Extract features with VGG16

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
model = VGG16(weights='imagenet', include_top=False)
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = model.predict(x)

Extract features from an arbitrary intermediate layer with VGG19

from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np
base_model = VGG19(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
block4_pool_features = model.predict(x)

Fine-tune InceptionV3 on a new set of classes

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(200, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
# train the model on the new data for a few epochs
model.fit_generator(...)
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
   print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(...)

Build InceptionV3 over a custom input tensor

from keras.applications.inception_v3 import InceptionV3
from keras.layers import Input
# this could also be the output a different Keras model or layer
input_tensor = Input(shape=(224, 224, 3))  # this assumes K.image_data_format() == 'channels_last'
model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True)

Documentation for individual models

The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset.

Depth refers to the topological depth of the network. This includes activation layers, batch normalization layers etc.

Xception

keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

Xception V1 model, with weights pre-trained on ImageNet.

On ImageNet, this model gets to a top-1 validation accuracy of 0.790and a top-5 validation accuracy of 0.945.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 299x299.

Arguments

• include_top: whether to include the fully-connected layer at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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 (299, 299, 3). It should have exactly 3 inputs channels, and width and height should be no smaller than 71. E.g. (150, 150, 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.

Returns

A Keras Model instance.

References

• Xception: Deep Learning with Depthwise Separable Convolutions

License

These weights are trained by ourselves and are released under the MIT license.

VGG16

keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

VGG16 model, with weights pre-trained on ImageNet.

This model can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• include_top: whether to include the 3 fully-connected layers at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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.

Returns

A Keras Model instance.

References

• Very Deep Convolutional Networks for Large-Scale Image Recognition: please cite this paper if you use the VGG models in your work.

License

These weights are ported from the ones released by VGG at Oxford under the Creative Commons Attribution License.

VGG19

keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

VGG19 model, with weights pre-trained on ImageNet.

This model can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• include_top: whether to include the 3 fully-connected layers at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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.

Returns

A Keras Model instance.

References

• Very Deep Convolutional Networks for Large-Scale Image Recognition

License

These weights are ported from the ones released by VGG at Oxford under the Creative Commons Attribution License.

ResNet

keras.applications.resnet.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.resnet.ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.resnet.ResNet152(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.resnet_v2.ResNet50V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.resnet_v2.ResNet101V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.resnet_v2.ResNet152V2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

ResNet, ResNetV2 models, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• include_top: whether to include the fully-connected layer at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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.

Returns

A Keras Model instance.

References

• ResNet: Deep Residual Learning for Image Recognition
• ResNetV2: Identity Mappings in Deep Residual Networks

License

These weights are ported from the following:

• ResNet: The original repository of Kaiming He under the MIT license.
• ResNetV2: Facebook under the BSD license.

InceptionV3

keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

Inception V3 model, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 299x299.

Arguments

• include_top: whether to include the fully-connected layer at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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 (299, 299, 3) (with 'channels_last' data format) or (3, 299, 299) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 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.

Returns

A Keras Model instance.

References

• Rethinking the Inception Architecture for Computer Vision

License

These weights are released under the Apache License.

InceptionResNetV2

keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

Inception-ResNet V2 model, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 299x299.

Arguments

• include_top: whether to include the fully-connected layer at the top of the network.
• weights: one of None (random initialization) or 'imagenet' (pre-training on ImageNet).
• 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 (299, 299, 3) (with 'channels_last' data format) or (3, 299, 299) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 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.

Returns

A Keras Model instance.

References

• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

License

These weights are released under the Apache License.

MobileNet

keras.applications.mobilenet.MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

MobileNet model, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• 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.
• alpha: controls the width of the network.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha = 1, default number of filters from the paper are used at each layer.
• depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier)
• dropout: dropout rate
• include_top: whether to include the fully-connected layer at the top of the network.
• weights: None (random initialization) or 'imagenet' (ImageNet weights)
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• pooling: Optional pooling mode for feature extraction when include_top is False.
  • None means that the output of the modelwill 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.

Returns

A Keras Model instance.

References

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

License

These weights are released under the Apache License.

DenseNet

keras.applications.densenet.DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.densenet.DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
keras.applications.densenet.DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)

DenseNet models, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• blocks: numbers of building blocks for the four dense layers.
• 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.

Returns

A Keras model instance.

References

• Densely Connected Convolutional Networks (CVPR 2017 Best Paper Award)

License

These weights are released under the BSD 3-clause License.

NASNet

keras.applications.nasnet.NASNetLarge(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)
keras.applications.nasnet.NASNetMobile(input_shape=None, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

Neural Architecture Search Network (NASNet) models, with weights pre-trained on ImageNet.

The default input size for the NASNetLarge model is 331x331 and for theNASNetMobile model is 224x224.

Arguments

• 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) for NASNetMobile or (331, 331, 3) (with 'channels_last' data format) or (3, 331, 331) (with 'channels_first' data format) for NASNetLarge. 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.
• include_top: whether to include the fully-connected layer at the top of the network.
• weights: None (random initialization) or 'imagenet' (ImageNet weights)
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• pooling: Optional pooling mode for feature extraction when include_top is False.
  • None means that the output of the modelwill 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.

Returns

A Keras Model instance.

References

• Learning Transferable Architectures for Scalable Image Recognition

License

These weights are released under the Apache License.

MobileNetV2

keras.applications.mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000)

MobileNetV2 model, with weights pre-trained on ImageNet.

This model and can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).

The default input size for this model is 224x224.

Arguments

• input_shape: optional shape tuple, to be specified if you would like to use a model with an input img resolution that is not (224, 224, 3). It should have exactly 3 inputs channels (224, 224, 3). You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. (160, 160, 3) would be one valid value.
• alpha: controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha = 1, default number of filters from the paper are used at each layer.
• 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.
• pooling: Optional pooling mode for feature extraction when include_top is False.
  • None means that the output of the modelwill 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.

Returns

A Keras model instance.

Raises

ValueError: in case of invalid argument for weights, or invalid input shape, alpha, rows when weights='imagenet'

References

• MobileNetV2: Inverted Residuals and Linear Bottlenecks

License

These weights are released under the Apache License.