DenseNetBackbone
classkeras_hub.models.DenseNetBackbone(
stackwise_num_repeats,
image_shape=(None, None, 3),
compression_ratio=0.5,
growth_rate=32,
**kwargs
)
Instantiates the DenseNet architecture.
This class implements a DenseNet backbone as described in Densely Connected Convolutional Networks (CVPR 2017).
Arguments
Examples
input_data = np.ones(shape=(8, 224, 224, 3))
# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("densenet121_imagenet")
model(input_data)
# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)
from_preset
methodDenseNetBackbone.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:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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
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 |
---|---|---|
densenet_121_imagenet | 7.04M | 121-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
densenet_169_imagenet | 12.64M | 169-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
densenet_201_imagenet | 18.32M | 201-layer DenseNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |