resnet_152_imagenet

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

• Deep Residual Learning for Image Recognition

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

• ResNetV1 Quickstart Notebook
• ResNetV1 API Documentation
• KerasHub Beginner Guide
• KerasHub Model Publishing Guide

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.

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')