tf.keras.applications.EfficientNetV2B3

Instantiates the EfficientNetV2B3 architecture.

tf.keras.applications.EfficientNetV2B3(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax',
    include_preprocessing=True
)

Reference:

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

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

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

include_topBoolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
weightsOne of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to "imagenet".
input_tensorOptional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
input_shapeOptional shape tuple, only to be specified if include_top is False. It should have exactly 3 inputs channels.
poolingOptional pooling mode for feature extraction when include_top is False. Defaults to None.

  • None means that the output of the model will be the 4D tensor output of the last convolutional layer.
  • "avg" means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor.
  • "max" means that global max pooling will be applied.
classesOptional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified. Defaults to 1000 (number of ImageNet classes).
classifier_activationA string 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. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".

A model instance.