BASNet Segmentation

[source]

BASNet class

keras_cv.models.BASNet(
    backbone,
    num_classes,
    input_shape=(None, None, 3),
    input_tensor=None,
    include_rescaling=False,
    projection_filters=64,
    prediction_heads=None,
    refinement_head=None,
    **kwargs
)

A Keras model implementing the BASNet architecture for semantic segmentation.

References

• BASNet: Boundary-Aware Segmentation Network for Mobile and Web Applications

Arguments

• backbone: keras.Model. The backbone network for the model that is used as a feature extractor for BASNet prediction encoder. Currently supported backbones are ResNet18 and ResNet34. Default backbone is keras_cv.models.ResNet34Backbone() (Note: Do not specify 'input_shape', 'input_tensor', or 'include_rescaling' within the backbone. Please provide these while initializing the 'BASNet' model.)
• num_classes: int, the number of classes for the segmentation model.
• input_shape: optional shape tuple, defaults to (None, None, 3).
• input_tensor: optional Keras tensor (i.e., output of layers.Input()) to use as image input for the model.
• include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
• 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.

Examples

import keras_cv

images = np.ones(shape=(1, 288, 288, 3))
labels = np.zeros(shape=(1, 288, 288, 1))

# Note: Do not specify 'input_shape', 'input_tensor', or
# 'include_rescaling' within the backbone.
backbone = keras_cv.models.ResNet34Backbone()
model = keras_cv.models.segmentation.BASNet(
    backbone=backbone,
    num_classes=1,
    input_shape=[288, 288, 3],
    include_rescaling=False
)

# Evaluate model
output = model(images)
pred_labels = output[0]

# Train model
model.compile(
    optimizer="adam",
    loss=keras.losses.BinaryCrossentropy(from_logits=False),
    metrics=["accuracy"],
)
model.fit(images, labels, epochs=3)
    ```


----

<span style="float:right;">[[source]](https://github.com/keras-team/keras-cv/tree/v0.8.2//keras_cv/models/task.py#L183)</span>

### `from_preset` method


```python
BASNet.from_preset()

Instantiate BASNet model from preset config and weights.

Arguments

• preset: string. Must be one of "resnet18", "resnet34", "basnet_resnet18", "basnet_resnet34". If looking for a preset with pretrained weights, choose one of "".
• load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.
• input_shape : input shape that will be passed to backbone initialization, Defaults to None.If None, the preset value will be used.

Examples

# Load architecture and weights from preset
model = keras_cv.models.BASNet.from_preset(
    "",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.BASNet.from_preset(
    "",
    load_weights=False,