► Keras 3 API documentation / KerasHub / Pretrained Models / MiT / MiTBackbone model

MiTBackbone model

[source]

MiTBackbone class

keras_hub.models.MiTBackbone(
    depths,
    num_layers,
    blockwise_num_heads,
    blockwise_sr_ratios,
    max_drop_path_rate,
    patch_sizes,
    strides,
    image_shape=(None, None, 3),
    hidden_dims=None,
    **kwargs
)

A backbone with feature pyramid outputs.

FeaturePyramidBackbone extends Backbone with a single pyramid_outputs property for accessing the feature pyramid outputs of the model. Subclassers should set the pyramid_outputs property during the model constructor.

Example

input_data = np.random.uniform(0, 256, size=(2, 224, 224, 3))

# Convert to feature pyramid output format using ResNet.
backbone = ResNetBackbone.from_preset("resnet50")
model = keras.Model(
    inputs=backbone.inputs, outputs=backbone.pyramid_outputs
)
model(input_data)  # A dict containing the keys ["P2", "P3", "P4", "P5"]

[source]

from_preset method

MiTBackbone.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 name Parameters Description
mit_b0_ade20k_512 3.32M MiT (MixTransformer) model with 8 transformer blocks.
mit_b1_ade20k_512 13.16M MiT (MixTransformer) model with 8 transformer blocks.
mit_b2_ade20k_512 24.20M MiT (MixTransformer) model with 16 transformer blocks.
mit_b3_ade20k_512 44.08M MiT (MixTransformer) model with 28 transformer blocks.
mit_b4_ade20k_512 60.85M MiT (MixTransformer) model with 41 transformer blocks.
mit_b5_ade20k_640 81.45M MiT (MixTransformer) model with 52 transformer blocks.
mit_b0_cityscapes_1024 3.32M MiT (MixTransformer) model with 8 transformer blocks.
mit_b1_cityscapes_1024 13.16M MiT (MixTransformer) model with 8 transformer blocks.
mit_b2_cityscapes_1024 24.20M MiT (MixTransformer) model with 16 transformer blocks.
mit_b3_cityscapes_1024 44.08M MiT (MixTransformer) model with 28 transformer blocks.
mit_b4_cityscapes_1024 60.85M MiT (MixTransformer) model with 41 transformer blocks.
mit_b5_cityscapes_1024 81.45M MiT (MixTransformer) model with 52 transformer blocks.
MiTBackbone model
MiTBackbone class
from_preset method