BASNetBackbone model

[source]

BASNetBackbone class

keras_hub.models.BASNetBackbone(
    image_encoder,
    num_classes,
    image_shape=(None, None, 3),
    projection_filters=64,
    prediction_heads=None,
    refinement_head=None,
    dtype=None,
    **kwargs
)

BASNet architecture for semantic segmentation.

A Keras model implementing the BASNet architecture described in BASNet: Boundary-Aware Segmentation Network for Mobile and Web Applications. BASNet uses a predict-refine architecture for highly accurate image segmentation.

Arguments

• image_encoder: A keras_hub.models.ResNetBackbone instance. The backbone network for the model that is used as a feature extractor for BASNet prediction encoder. Currently supported backbones are ResNet18 and ResNet34. (Note: Do not specify image_shape within the backbone. Please provide these while initializing the 'BASNetBackbone' model)
• num_classes: int, the number of classes for the segmentation model.
• image_shape: optional shape tuple, defaults to (None, None, 3).
• projection_filters: int, number of filters in the convolution layer projecting low-level features from the backbone.
• prediction_heads: (Optional) List of keras.layers.Layer defining the prediction module head for the model. If not provided, a default head is created with a Conv2D layer followed by resizing.
• refinement_head: (Optional) a keras.layers.Layer defining the refinement module head for the model. If not provided, a default head is created with a Conv2D layer.
• dtype: None or str or keras.mixed_precision.DTypePolicy. The dtype to use for the model's computations and weights.

[source]

from_preset method

BASNetBackbone.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,
)