ImageClassifier

• Original Link : https://keras.io/api/keras_hub/base_classes/image_classifier/
• Last Checked at : 2024-11-26

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ImageClassifier class

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

Base class for all image classification tasks.

ImageClassifier tasks wrap a keras_hub.models.Backbone and a keras_hub.models.Preprocessor to create a model that can be used for image classification. ImageClassifier 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). All ImageClassifier tasks include a from_preset() constructor which can be used to load a pre-trained config and weights.

Arguments

• backbone: A keras_hub.models.Backbone 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: "avg" or "max". The type of pooling to apply on backbone output. Defaults to average pooling.
• 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.ImageClassifier.from_preset(
    "resnet_50_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.ImageClassifier.from_preset(
    "resnet_50_imagenet"
)
classifier.fit(x=images, y=labels, batch_size=2)

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

classifier = keras_hub.models.ImageClassifier.from_preset(
    "resnet_50_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]
backbone = keras_hub.models.ResNetBackbone(
    stackwise_num_filters=[64, 64, 64],
    stackwise_num_blocks=[2, 2, 2],
    stackwise_num_strides=[1, 2, 2],
    block_type="basic_block",
    use_pre_activation=True,
    pooling="avg",
)
classifier = keras_hub.models.ImageClassifier(
    backbone=backbone,
    num_classes=4,
)
classifier.fit(x=images, y=labels, batch_size=2)

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from_preset method

ImageClassifier.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,
)

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compile method

ImageClassifier.compile(optimizer="auto", loss="auto", metrics="auto", **kwargs)

Configures the ImageClassifier task for training.

The ImageClassifier task extends the default compilation signature of keras.Model.compile with defaults for optimizer, loss, and metrics. To override these defaults, pass any value to these arguments during compilation.

Arguments

• optimizer: "auto", an optimizer name, or a keras.Optimizer instance. Defaults to "auto", which uses the default optimizer for the given model and task. See keras.Model.compile and keras.optimizers for more info on possible optimizer values.
• loss: "auto", a loss name, or a keras.losses.Loss instance. Defaults to "auto", where a keras.losses.SparseCategoricalCrossentropy loss will be applied for the classification task. See keras.Model.compile and keras.losses for more info on possible loss values.
• metrics: "auto", or a list of metrics to be evaluated by the model during training and testing. Defaults to "auto", where a keras.metrics.SparseCategoricalAccuracy will be applied to track the accuracy of the model during training. See keras.Model.compile and keras.metrics for more info on possible metrics values.
• **kwargs: See keras.Model.compile for a full list of arguments supported by the compile method.

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save_to_preset method

ImageClassifier.save_to_preset(preset_dir)

Save task to a preset directory.

Arguments

• preset_dir: The path to the local model preset directory.

preprocessor property

keras_hub.models.ImageClassifier.preprocessor

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

backbone property

keras_hub.models.ImageClassifier.backbone

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