ResNetBackbone model

• Original Link : https://keras.io/api/keras_hub/models/resnet/resnet_backbone/
• Last Checked at : 2024-11-26

source

ResNetBackbone class

keras_hub.models.ResNetBackbone(
    input_conv_filters,
    input_conv_kernel_sizes,
    stackwise_num_filters,
    stackwise_num_blocks,
    stackwise_num_strides,
    block_type,
    use_pre_activation=False,
    image_shape=(None, None, 3),
    data_format=None,
    dtype=None,
    **kwargs
)

ResNet and ResNetV2 core network with hyperparameters.

This class implements a ResNet backbone as described in Deep Residual Learning for Image Recognition( CVPR 2016), Identity Mappings in Deep Residual Networks(ECCV 2016), ResNet strikes back: An improved training procedure in timm( NeurIPS 2021 Workshop) and Bag of Tricks for Image Classification with Convolutional Neural Networks.

The difference in ResNet and ResNetV2 rests in the structure of their individual building blocks. In ResNetV2, the batch normalization and ReLU activation precede the convolution layers, as opposed to ResNet where the batch normalization and ReLU activation are applied after the convolution layers.

ResNetVd introduces two key modifications to the standard ResNet. First, the initial convolutional layer is replaced by a series of three successive convolutional layers. Second, shortcut connections use an additional pooling operation rather than performing downsampling within the convolutional layers themselves.

Arguments

• input_conv_filters: list of ints. The number of filters of the initial convolution(s).
• input_conv_kernel_sizes: list of ints. The kernel sizes of the initial convolution(s).
• stackwise_num_filters: list of ints. The number of filters for each stack.
• stackwise_num_blocks: list of ints. The number of blocks for each stack.
• stackwise_num_strides: list of ints. The number of strides for each stack.
• block_type: str. The block type to stack. One of "basic_block", "bottleneck_block", "basic_block_vd" or "bottleneck_block_vd". Use "basic_block" for ResNet18 and ResNet34. Use "bottleneck_block" for ResNet50, ResNet101 and ResNet152 and the "_vd" prefix for the respective ResNet_vd variants.
• use_pre_activation: boolean. Whether to use pre-activation or not. True for ResNetV2, False for ResNet.
• image_shape: tuple. The input shape without the batch size. Defaults to (None, None, 3).
• data_format: None or str. If specified, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch_size, height, width, channels) while "channels_first" corresponds to inputs with shape (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
• dtype: None or str or keras.mixed_precision.DTypePolicy. The dtype to use for the model’s computations and weights.

Examples

input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
# Pretrained ResNet backbone.
model = keras_hub.models.ResNetBackbone.from_preset("resnet_50_imagenet")
model(input_data)
# Randomly initialized ResNetV2 backbone with a custom config.
model = keras_hub.models.ResNetBackbone(
    input_conv_filters=[64],
    input_conv_kernel_sizes=[7],
    stackwise_num_filters=[64, 64, 64],
    stackwise_num_blocks=[2, 2, 2],
    stackwise_num_strides=[1, 2, 2],
    block_type="basic_block",
    use_pre_activation=True,
)
model(input_data)

source

from_preset method

ResNetBackbone.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,
)