VGGImageClassifier model

[source]

VGGImageClassifier class

keras_hub.models.VGGImageClassifier(
    backbone,
    num_classes,
    preprocessor=None,
    pooling="avg",
    pooling_hidden_dim=4096,
    activation=None,
    dropout=0.0,
    head_dtype=None,
    **kwargs
)

VGG image classification task.

VGGImageClassifier tasks wrap a keras_hub.models.VGGBackbone and a keras_hub.models.Preprocessor to create a model that can be used for image classification. VGGImageClassifier tasks take an additional num_classes argument, controlling the number of predicted output classes.

To fine-tune with fit(), pass a dataset containing tuples of (x, y) labels where x is a string and y is a integer from [0, num_classes).

Not that unlike keras_hub.model.ImageClassifier, the VGGImageClassifier allows and defaults to pooling="flatten", when inputs are flatten and passed through two intermediate dense layers before the final output projection.

Arguments

• backbone: A keras_hub.models.VGGBackbone instance or a keras.Model.
• num_classes: int. The number of classes to predict.
• preprocessor: None, a keras_hub.models.Preprocessor instance, a keras.Layer instance, or a callable. If None no preprocessing will be applied to the inputs.
• pooling: "flatten", "avg", or "max". The type of pooling to apply on backbone output. The default is flatten to match the original VGG implementation, where backbone inputs will be flattened and passed through two dense layers with a "relu" activation.
• pooling_hidden_dim: the output feature size of the pooling dense layers. This only applies when pooling="flatten".
• activation: None, str, or callable. The activation function to use on the Dense layer. Set activation=None to return the output logits. Defaults to "softmax".
• head_dtype: None, str, or keras.mixed_precision.DTypePolicy. The dtype to use for the classification head's computations and weights.

Examples

Call predict() to run inference.

# Load preset and train
images = np.random.randint(0, 256, size=(2, 224, 224, 3))
classifier = keras_hub.models.VGGImageClassifier.from_preset(
    "vgg_16_imagenet"
)
classifier.predict(images)

Call fit() on a single batch.

# Load preset and train
images = np.random.randint(0, 256, size=(2, 224, 224, 3))
labels = [0, 3]
classifier = keras_hub.models.VGGImageClassifier.from_preset(
    "vgg_16_imagenet"
)
classifier.fit(x=images, y=labels, batch_size=2)

Call fit() with custom loss, optimizer and backbone.

classifier = keras_hub.models.VGGImageClassifier.from_preset(
    "vgg_16_imagenet"
)
classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(5e-5),
)
classifier.backbone.trainable = False
classifier.fit(x=images, y=labels, batch_size=2)

Custom backbone.

images = np.random.randint(0, 256, size=(2, 224, 224, 3))
labels = [0, 3]
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),
)
classifier = keras_hub.models.VGGImageClassifier(
    backbone=backbone,
    num_classes=4,
)
classifier.fit(x=images, y=labels, batch_size=2)

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from_preset method

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

Instantiate a keras_hub.models.Task 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 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'

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

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

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, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
    "gemma_2b_en",
)

# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
    "bert_base_en",
    num_classes=2,
)

backbone property

keras_hub.models.VGGImageClassifier.backbone

A keras_hub.models.Backbone model with the core architecture.

preprocessor property

keras_hub.models.VGGImageClassifier.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.