► Keras 3 API documentation / KerasCV / Models / Backbones / ResNetV1 backbones

ResNetV1 backbones

[source]

ResNetBackbone class

keras_cv.models.ResNetBackbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

Instantiates the ResNet architecture.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • stackwise_filters: list of ints, number of filters for each stack in the model.
  • stackwise_blocks: list of ints, number of blocks for each stack in the model.
  • stackwise_strides: list of ints, stride for each stack in 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.
  • 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.
  • block_type: string, one of "basic_block" or "block". The block type to stack. Use "basic_block" for ResNet18 and ResNet34.

Examples

input_data = tf.ones(shape=(8, 224, 224, 3))

# Pretrained backbone
model = keras_cv.models.ResNetBackbone.from_preset("resnet50_imagenet")
output = model(input_data)

# Randomly initialized backbone with a custom config
model = ResNetBackbone(
    stackwise_filters=[64, 128, 256, 512],
    stackwise_blocks=[2, 2, 2, 2],
    stackwise_strides=[1, 2, 2, 2],
    include_rescaling=False,
)
output = model(input_data)

[source]

from_preset method

ResNetBackbone.from_preset()

Instantiate ResNetBackbone model from preset config and weights.

Arguments

  • preset: string. Must be one of "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet50_imagenet". If looking for a preset with pretrained weights, choose one of "resnet50_imagenet".
  • load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.

Examples

# Load architecture and weights from preset
model = keras_cv.models.ResNetBackbone.from_preset(
    "resnet50_imagenet",
)

# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.ResNetBackbone.from_preset(
    "resnet50_imagenet",
    load_weights=False,
Preset name Parameters Description
resnet18 11.19M ResNet model with 18 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style).
resnet34 21.30M ResNet model with 34 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style).
resnet50 23.56M ResNet model with 50 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style).
resnet101 42.61M ResNet model with 101 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style).
resnet152 58.30M ResNet model with 152 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style).
resnet50_imagenet 23.56M ResNet model with 50 layers where the batch normalization and ReLU activation are applied after the convolution layers (v1 style). Trained on Imagenet 2012 classification task.

[source]

ResNet18Backbone class

keras_cv.models.ResNet18Backbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

ResNetBackbone (V1) model with 18 layers.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • 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.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Randomly initialized backbone
model = ResNet18Backbone()
output = model(input_data)

[source]

ResNet34Backbone class

keras_cv.models.ResNet34Backbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

ResNetBackbone (V1) model with 34 layers.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • 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.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Randomly initialized backbone
model = ResNet34Backbone()
output = model(input_data)

[source]

ResNet50Backbone class

keras_cv.models.ResNet50Backbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

ResNetBackbone (V1) model with 50 layers.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • 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.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Randomly initialized backbone
model = ResNet50Backbone()
output = model(input_data)

[source]

ResNet101Backbone class

keras_cv.models.ResNet101Backbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

ResNetBackbone (V1) model with 101 layers.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • 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.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Randomly initialized backbone
model = ResNet101Backbone()
output = model(input_data)

[source]

ResNet152Backbone class

keras_cv.models.ResNet152Backbone(
    stackwise_filters,
    stackwise_blocks,
    stackwise_strides,
    include_rescaling,
    input_shape=(None, None, 3),
    input_tensor=None,
    block_type="block",
    **kwargs
)

ResNetBackbone (V1) model with 152 layers.

Reference

The difference in ResNetV1 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 ResNetV1 where the batch normalization and ReLU activation are applied after the convolution layers.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Arguments

  • include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
  • 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.

Example

input_data = tf.ones(shape=(8, 224, 224, 3))

# Randomly initialized backbone
model = ResNet152Backbone()
output = model(input_data)
ResNetV1 backbones
ResNetBackbone class
from_preset method
ResNet18Backbone class
ResNet34Backbone class
ResNet50Backbone class
ResNet101Backbone class
ResNet152Backbone class