ResNetV1 主干网络
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 aRescaling(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)
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. |
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 aRescaling(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)
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 aRescaling(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)
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 aRescaling(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)
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 aRescaling(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)
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 aRescaling(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)