ResNetBackbone
classkeras_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
"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.True
for ResNetV2, False
for ResNet.(None, None, 3)
.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"
.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)
from_preset
methodResNetBackbone.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 |
---|---|---|
resnet_18_imagenet | 11.19M | 18-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_50_imagenet | 23.56M | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_101_imagenet | 42.61M | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_152_imagenet | 58.30M | 152-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_v2_50_imagenet | 23.56M | 50-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_v2_101_imagenet | 42.61M | 101-layer ResNetV2 model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_18_imagenet | 11.72M | 18-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_34_imagenet | 21.84M | 34-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_50_imagenet | 25.63M | 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_50_ssld_imagenet | 25.63M | 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation. |
resnet_vd_50_ssld_v2_imagenet | 25.63M | 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation and AutoAugment. |
resnet_vd_50_ssld_v2_fix_imagenet | 25.63M | 50-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head. |
resnet_vd_101_imagenet | 44.67M | 101-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_101_ssld_imagenet | 44.67M | 101-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation. |
resnet_vd_152_imagenet | 60.36M | 152-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
resnet_vd_200_imagenet | 74.93M | 200-layer ResNetVD (ResNet with bag of tricks) model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |