VGGBackbone model

[source]

VGGBackbone class

keras_hub.models.VGGBackbone(
    stackwise_num_repeats, stackwise_num_filters, image_shape=(None, None, 3), **kwargs
)

This class represents Keras Backbone of VGG model.

This class implements a VGG backbone as described in Very Deep Convolutional Networks for Large-Scale Image Recognition(ICLR 2015).

Arguments

• stackwise_num_repeats: list of ints, number of repeated convolutional blocks per VGG block. For VGG16 this is [2, 2, 3, 3, 3] and for VGG19 this is [2, 2, 4, 4, 4].
• stackwise_num_filters: list of ints, filter size for convolutional blocks per VGG block. For both VGG16 and VGG19 this is [ 64, 128, 256, 512, 512].
• image_shape: tuple, optional shape tuple, defaults to (None, None, 3).

Examples

input_data = np.ones((2, 224, 224, 3), dtype="float32")

# Pretrained VGG backbone.
model = keras_hub.models.VGGBackbone.from_preset("vgg16")
model(input_data)

# Randomly initialized VGG backbone with a custom config.
model = keras_hub.models.VGGBackbone(
    stackwise_num_repeats = [2, 2, 3, 3, 3],
    stackwise_num_filters = [64, 128, 256, 512, 512],
    image_shape = (224, 224, 3),
)
model(input_data)

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from_preset method

VGGBackbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Backbone from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)