MetaCLIP2ImageConverter

[source]

MetaCLIP2ImageConverter class

keras_hub.layers.MetaCLIP2ImageConverter(
    image_size=None,
    scale=None,
    offset=None,
    crop_to_aspect_ratio=True,
    pad_to_aspect_ratio=False,
    interpolation="bilinear",
    antialias=False,
    bounding_box_format="yxyx",
    data_format=None,
    **kwargs
)

Image converter for MetaCLIP 2 models.

This converter handles image preprocessing for MetaCLIP 2, including resizing and normalization to match the model's expected input format.

[source]

from_preset method

MetaCLIP2ImageConverter.from_preset(preset, **kwargs)

Instantiate a keras_hub.layers.ImageConverter 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 'pali_gemma_3b_224'
2. a Kaggle Models handle like 'kaggle://user/paligemma/keras/pali_gemma_3b_224'
3. a Hugging Face handle like 'hf://user/pali_gemma_3b_224'
4. a path to a local preset directory like './pali_gemma_3b_224'

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

batch = np.random.randint(0, 256, size=(2, 512, 512, 3))

# Resize images for `"pali_gemma_3b_224"`.
converter = keras_hub.layers.ImageConverter.from_preset(
    "pali_gemma_3b_224"
)
converter(batch) # # Output shape (2, 224, 224, 3)

# Resize images for `"pali_gemma_3b_448"` without cropping.
converter = keras_hub.layers.ImageConverter.from_preset(
    "pali_gemma_3b_448",
    crop_to_aspect_ratio=False,
)
converter(batch) # # Output shape (2, 448, 448, 3)