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.