densenet_169_imagenet

Model Overview

DenseNet is a convolution network which densely connects each layer to every other layer in a feed-forward fashion. The model was originally evaluated on four object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). See the model card below for benchmarks, data sources, and intended use cases. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.

Weights are released under the 3-Clause BSD License. Keras model code is released under the Apache 2 License.

Links

• DenseNet Quickstart Notebook
• DenseNet API Documentation
• DenseNet Model Card
• 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

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

# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("densenet_169_imagenet")
model(input_data)

# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
    stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)

# Use densenet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("densenet_169_imagenet")

# Use Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/densenet121.tv_in1k')

Example Usage with Hugging Face URI

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

# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("hf://keras/densenet_169_imagenet")
model(input_data)

# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
    stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)

# Use densenet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/densenet_169_imagenet")

# Use Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/densenet121.tv_in1k')