► KerasHub: Pretrained Models / API documentation / Model Architectures / Segment Anything Model / SAMBackbone model

SAMBackbone model

[source]

SAMBackbone class

keras_hub.models.SAMBackbone(
    image_encoder, prompt_encoder, mask_decoder, dtype=None, **kwargs
)

A backbone for the Segment Anything Model (SAM).

Arguments

Example

image_size=128
batch_size=2
input_data = {
    "images": np.ones(
        (batch_size, image_size, image_size, 3),
        dtype="float32",
    ),
    "points": np.ones((batch_size, 1, 2), dtype="float32"),
    "labels": np.ones((batch_size, 1), dtype="float32"),
    "boxes": np.ones((batch_size, 1, 2, 2), dtype="float32"),
    "masks": np.zeros(
        (batch_size, 0, image_size, image_size, 1)
    ),
}
image_encoder = keras_hub.models.ViTDetBackbone(
    hidden_size=16,
    num_layers=16,
    intermediate_dim=16 * 4,
    num_heads=16,
    global_attention_layer_indices=[2, 5, 8, 11],
    patch_size=16,
    num_output_channels=8,
    window_size=2,
    image_shape=(image_size, image_size, 3),
)
prompt_encoder = keras_hub.layers.SAMPromptEncoder(
    hidden_size=8,
    image_embedding_size=(8, 8),
    input_image_size=(
        image_size,
        image_size,
    ),
    mask_in_channels=16,
)
mask_decoder = keras_hub.layers.SAMMaskDecoder(
    num_layers=2,
    hidden_size=8,
    intermediate_dim=32,
    num_heads=8,
    embedding_dim=8,
    num_multimask_outputs=3,
    iou_head_depth=3,
    iou_head_hidden_dim=8,
)
backbone = keras_hub.models.SAMBackbone(
    image_encoder=image_encoder,
    prompt_encoder=prompt_encoder,
    mask_decoder=mask_decoder,
    image_shape=(image_size, image_size, 3),
)
backbone(input_data)

[source]

from_preset method

SAMBackbone.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,
)
Preset Parameters Description
sam_base_sa1b 93.74M The base SAM model trained on the SA1B dataset.
sam_huge_sa1b 312.34M The huge SAM model trained on the SA1B dataset.
sam_large_sa1b 641.09M The large SAM model trained on the SA1B dataset.
SAMBackbone model
SAMBackbone class
from_preset method