darknet_53_imagenet

keras/darknet_53_imagenet

darknet_53_imagenet is a machine learning model from keras. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras-hub.

This class represents the CSPDarkNet architecture.

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At a glance

Library: keras-hub
Access: Public
Created: Jun 16, 2025
Updated: Jun 17, 2025
SHA: c512381c

Model card

From the Hugging Face model README

Model Overview

This class represents the CSPDarkNet architecture.

Reference

CSPNet Paper

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

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
`csp_darknet_53_ra_imagenet`	27642184	A CSP-DarkNet (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.
`csp_resnext_50_ra_imagenet`	20569896	A CSP-ResNeXt (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.
`csp_resnet_50_ra_imagenet`	21616168	A CSP-ResNet (Cross-Stage-Partial) image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.
`darknet_53_imagenet`	41609928	A DarkNet image classification model pre-trained on the Randomly Augmented ImageNet 1k dataset at a 256x256 resolution.

Example Usage

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

# Pretrained backbone
model = keras_hub.models.CSPNetBackbone.from_preset("darknet_53_imagenet")
model(input_data)

# Randomly initialized backbone with a custom config
    model = keras_hub.models.CSPNetBackbone(
        stem_filters=32,
        stem_kernel_size=3,
        stem_strides=1,
        stackwise_depth=[1, 2, 4],
        stackwise_strides=[1, 2, 2],
        stackwise_num_filters=[32, 64, 128],
        block_type="dark",
)

model(input_data)

#Use cspnet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("darknet_53_imagenet")

#Use Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/cspdarknet53.ra_in1k')

Example Usage with Hugging Face URI

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

# Pretrained backbone
model = keras_hub.models.CSPNetBackbone.from_preset("hf://keras/darknet_53_imagenet")
model(input_data)

# Randomly initialized backbone with a custom config
    model = keras_hub.models.CSPNetBackbone(
        stem_filters=32,
        stem_kernel_size=3,
        stem_strides=1,
        stackwise_depth=[1, 2, 4],
        stackwise_strides=[1, 2, 2],
        stackwise_num_filters=[32, 64, 128],
        block_type="dark",
)

model(input_data)

#Use cspnet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/darknet_53_imagenet")

#Use Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/cspdarknet53.ra_in1k')

Library: keras-hub
Created: Jun 16, 2025
Updated: Jun 17, 2025