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EfficientNet-Lite

EfficientNet-Lite is designed for performance on mobile CPU, GPU and EdgeTPU.

Differences from EfficientNet: - Removed squeeze-and-excitation networks

More information on the original EfficientNet-Lite can be found here.

applications.efficientnet_lite.EfficientNet_lite0

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applications.efficientnet_lite.EfficientNet_lite0(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax')

Reference paper: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.

applications.efficientnet_lite.EfficientNet_lite1

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applications.efficientnet_lite.EfficientNet_lite1(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax')

Reference paper: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.

applications.efficientnet_lite.EfficientNet_lite2

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applications.efficientnet_lite.EfficientNet_lite2(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax')

Reference paper: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.

applications.efficientnet_lite.EfficientNet_lite3

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applications.efficientnet_lite.EfficientNet_lite3(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax')

Reference paper: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.

applications.efficientnet_lite.EfficientNet_lite4

1
applications.efficientnet_lite.EfficientNet_lite4(include_top=True, weights='hasc', input_shape=None, pooling=None, classes=6, classifier_activation='softmax')

Reference paper: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

By default, it loads weights pre-trained on HASC. Check 'weights' for other options.

The default input size for this model is 768 (256 * 3).

Arguments

  • include_top : whether to include the fully-connected layer at the top of the network.
  • weights : one of 'None' (he_normal initialization), 'hasc' (pre-training on HASC), or the path to the weights file to be loaded.
  • input_shape : optional shape tuple, default (768, 1) (with channels_last data format).
  • pooling : optional pooling mode for feature extraction when include_top is False.
    • None means that the output of the model will be applied to the 3D tensor output of the last convolutional block.
    • avg means that global average pooling will be applied to the output of the last convolutioinal 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, default 6.
  • 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, default 'softmax'.

Returns

  • tensorflow.keras.Model instance.