KerasHub

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KerasHub is a pretrained modeling library that aims to be simple, flexible, and fast. The library provides Keras 3 implementations of popular model architectures, paired with a collection of pretrained checkpoints available on Kaggle Models. Models can be used for both training and inference, on any of the TensorFlow, Jax, and Torch backends.

KerasHub is an extension of the core Keras API; KerasHub components are provided as keras.layers.Layer and keras.Model implementations. If you are familiar with Keras, congratulations! You already understand most of KerasHub.

Quick links

• Getting started with KerasHub
• Developer guides
• API documentation
• KerasHub on GitHub
• KerasHub models on Kaggle
• Pretrained model list

Installation

To install the latest KerasHub release with Keras 3, simply run:

pip install --upgrade keras-hub

To install the latest nightly changes for both KerasHub and Keras, you can use our nightly package.

pip install --upgrade keras-hub-nightly

Currently, installing KerasHub will always pull in TensorFlow for use of the tf.data API for preprocessing. When pre-processing with tf.data, training can still happen on any backend.

Visit the core Keras getting started page for more information on installing Keras 3, accelerator support, and compatibility with different frameworks.

Quickstart

Choose a backend:

import os
os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!

Import KerasHub and other libraries:

import keras
import keras_hub
import numpy as np
import tensorflow_datasets as tfds

Load a resnet model and use it to predict a label for an image:

classifier = keras_hub.models.ImageClassifier.from_preset(
    "resnet_50_imagenet",
    activation="softmax",
)
url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"
path = keras.utils.get_file(origin=url)
image = keras.utils.load_img(path)
preds = classifier.predict(np.array([image]))
print(keras_hub.utils.decode_imagenet_predictions(preds))

Load a Bert model and fine-tune it on IMDb movie reviews:

classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_base_en_uncased",
    activation="softmax",
    num_classes=2,
)
imdb_train, imdb_test = tfds.load(
    "imdb_reviews",
    split=["train", "test"],
    as_supervised=True,
    batch_size=16,
)
classifier.fit(imdb_train, validation_data=imdb_test)
preds = classifier.predict(["What an amazing movie!", "A total waste of time."])
print(preds)

Compatibility

We follow Semantic Versioning, and plan to provide backwards compatibility guarantees both for code and saved models built with our components. While we continue with pre-release 0.y.z development, we may break compatibility at any time and APIs should not be consider stable.

Disclaimer

KerasHub provides access to pre-trained models via the keras_hub.models API. These pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind.

Citing KerasHub

If KerasHub helps your research, we appreciate your citations. Here is the BibTeX entry:

@misc{kerashub2024,
  title={KerasHub},
  author={Watson, Matthew, and  Chollet, Fran\c{c}ois and Sreepathihalli,
  Divyashree, and Saadat, Samaneh and Sampath, Ramesh, and Rasskin, Gabriel and
  and Zhu, Scott and Singh, Varun and Wood, Luke and Tan, Zhenyu and Stenbit,
  Ian and Qian, Chen, and Bischof, Jonathan and others},
  year={2024},
  howpublished={\url{https://github.com/keras-team/keras-hub}},
}