ResNetImageClassifierPreprocessor layer

• Original Link : https://keras.io/api/keras_hub/models/resnet/resnet_image_classifier_preprocessor/
• Last Checked at : 2024-11-26

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ResNetImageClassifierPreprocessor class

keras_hub.models.ResNetImageClassifierPreprocessor(image_converter=None, **kwargs)

Base class for image classification preprocessing layers.

ImageClassifierPreprocessor tasks wraps a keras_hub.layers.ImageConverter to create a preprocessing layer for image classification tasks. It is intended to be paired with a keras_hub.models.ImageClassifier task.

All ImageClassifierPreprocessor take inputs three inputs, x, y, and sample_weight. x, the first input, should always be included. It can be a image or batch of images. See examples below. y and sample_weight are optional inputs that will be passed through unaltered. Usually, y will be the classification label, and sample_weight will not be provided.

The layer will output either x, an (x, y) tuple if labels were provided, or an (x, y, sample_weight) tuple if labels and sample weight were provided. x will be the input images after all model preprocessing has been applied.

All ImageClassifierPreprocessor tasks include a from_preset() constructor which can be used to load a pre-trained config and vocabularies. You can call the from_preset() constructor directly on this base class, in which case the correct class for your model will be automatically instantiated.

Examples.

preprocessor = keras_hub.models.ImageClassifierPreprocessor.from_preset(
    "resnet_50",
)
# Resize a single image for resnet 50.
x = np.random.randint(0, 256, (512, 512, 3))
x = preprocessor(x)
# Resize a labeled image.
x, y = np.random.randint(0, 256, (512, 512, 3)), 1
x, y = preprocessor(x, y)
# Resize a batch of labeled images.
x, y = [np.random.randint(0, 256, (512, 512, 3)), np.zeros((512, 512, 3))], [1, 0]
x, y = preprocessor(x, y)
# Use a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
ds = tf.data.Dataset.from_tensor_slices((x, y)).batch(2)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

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from_preset method

ResNetImageClassifierPreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.models.Preprocessor 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 Preprocessor subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)
# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
    "bert_base_en",
)

image_converter property

keras_hub.models.ResNetImageClassifierPreprocessor.image_converter

The image converter used to preprocess image data.