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Namespace Keras.Applications

Classes

DenseNet

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.

ImageNetPrediction

InceptionResNetV2

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.

InceptionV3

MobileNet

MobileNet model, with weights pre-trained on ImageNet. Note that this model only supports the data format 'channels_last' (height, width, channels). The default input size for this model is 224x224.

MobileNetV2

MobileNet model, with weights pre-trained on ImageNet. Note that this model only supports the data format 'channels_last' (height, width, channels). The default input size for this model is 224x224.

NASNet

Neural Architecture Search Network (NASNet) models, with weights pre-trained on ImageNet. The default input size for the NASNetLarge model is 331x331 and for the NASNetMobile model is 224x224.

ResNet

ResNet, ResNetV2, ResNeXt 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.

ResNetNext

ResNet, ResNetV2, ResNeXt 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.

ResNetV2

ResNet, ResNetV2, ResNeXt 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.

VGG16

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.

VGG19

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.

Xception

Xception V1 model, with weights pre-trained on ImageNet. On ImageNet, this model gets to a top-1 validation accuracy of 0.790 and a top-5 validation accuracy of 0.945. Note that this model only supports the data format 'channels_last' (height, width, channels). The default input size for this model is 299x299.

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