library_name: keras-hub
license: apache-2.0
tags:
- image-classification
Model Overview
Instantiates the ResNet architecture. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
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.
Links
coming soon
Installation
Keras and KerasHub can be installed with:
pip install -U -q keras-hub
pip install -U -q keras
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.
Presets
The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co./timm. Full code examples for each are available below.
| 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 | 52-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution |
Example Usage
# Pretrained ResNet backbone.
model = keras_hub.models.ResNetBackbone.from_preset("resnet_152_imagenet")
input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
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)
# Use resnet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("resnet_152_imagenet")
# User timm presets directly from hugingface
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnet101.a1_in1k')
Example Usage with Hugging Face URI
# Pretrained ResNet backbone.
model = keras_hub.models.ResNetBackbone.from_preset("hf://keras/resnet_152_imagenet")
input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
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)
# Use resnet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/resnet_152_imagenet")
# User timm presets directly from hugingface
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/resnet101.a1_in1k')