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 use 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 provide as Layers and Models. If you are familiar with Keras, congratulations! You already understand most of KerasHub.

See our Getting Started guide to start learning our API. We welcome contributions.

Quick links

• KerasHub API reference
• KerasHub on GitHub
• KerasHub models on Kaggle
• List of available pretrained models

Guides

• Getting Started with KerasHub
• Classification with KerasHub
• Segment Anything in KerasHub
• Semantic Segmentation with KerasHub
• Stable Diffusion 3 in KerasHub
• Pretraining a Transformer from scratch with KerasHub
• Uploading Models with KerasHub

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

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

Read Getting started with Keras for more information on installing Keras 3 and compatibility with different frameworks.

Note: We recommend using KerasHub with TensorFlow 2.16 or later, as TF 2.16 packages Keras 3 by default.

Quickstart

Below is a quick example using ResNet to predict an image, and BERT to train a classifier:

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

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

# Load a ResNet model.
classifier = keras_hub.models.ImageClassifier.from_preset(
    "resnet_50_imagenet",
    activation="softmax",
)
# Predict a label for a single image.
image_url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"
image_path = keras.utils.get_file(origin=image_url)
image = keras.utils.load_img(image_path)
batch = np.array([image])
preds = classifier.predict(batch)
print(keras_hub.utils.decode_imagenet_predictions(preds))

# Load a BERT model.
classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_base_en_uncased",
    activation="softmax",
    num_classes=2,
)

# Fine-tune on IMDb movie reviews.
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)
# Predict two new examples.
preds = classifier.predict(
    ["What an amazing movie!", "A total waste of my 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}},
}