DeepLabV3ImageSegmenter model

• 원본 링크 : https://keras.io/api/keras_hub/models/deeplab_v3/deeplab_v3_image_segmenter/
• 최종 확인 : 2024-11-25

source

DeepLabV3ImageSegmenter class

keras_hub.models.DeepLabV3ImageSegmenter(
    backbone, num_classes, activation=None, preprocessor=None, **kwargs
)

DeepLabV3 and DeeplabV3 and DeeplabV3Plus segmentation task.

Arguments

• backbone: A keras_hub.models.DeepLabV3 instance.
• num_classes: int. The number of classes for the detection model. Note that the num_classes contains the background class, and the classes from the data should be represented by integers with range [0, num_classes].
• activation: str or callable. The activation function to use on the Dense layer. Set activation=None to return the output logits. Defaults to None.
• preprocessor: A keras_hub.models.DeepLabV3ImageSegmenterPreprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model.

Example

Load a DeepLabV3 preset with all the 21 class, pretrained segmentation head.

images = np.ones(shape=(1, 96, 96, 3))
labels = np.zeros(shape=(1, 96, 96, 1))
segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "deeplabv3_resnet50_pascalvoc",
)
segmenter.predict(images)

Specify num_classes to load randomly initialized segmentation head.

segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "deeplabv3_resnet50_pascalvoc",
    num_classes=2,
)
segmenter.fit(images, labels, epochs=3)
segmenter.predict(images)  # Trained 2 class segmentation.

Load DeepLabv3+ presets a extension of DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries.

segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "deeplabv3_plus_resnet50_pascalvoc",
)
segmenter.predict(images)

source

from_preset method

DeepLabV3ImageSegmenter.from_preset(preset, load_weights=True, **kwargs)

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

This constructor can be called in one of two ways. Either from a task specific base class like keras_hub.models.CausalLM.from_preset(), or from a model class like keras_hub.models.BertTextClassifier.from_preset(). If calling from the a base class, the subclass of the returning object will be inferred from the config in the preset directory.

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, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
    "gemma_2b_en",
)
# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
    "bert_base_en",
    num_classes=2,
)

backbone property

keras_hub.models.DeepLabV3ImageSegmenter.backbone

A keras_hub.models.Backbone model with the core architecture.

preprocessor property

keras_hub.models.DeepLabV3ImageSegmenter.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.