» KerasCV
KerasCV
KerasCV is a toolbox of modular building blocks (layers, metrics, losses, data augmentation) that computer vision engineers can leverage to quickly assemble production-grade, state-of-the-art training and inference pipelines for common use cases such as image classification, object detection, image segmentation, image data augmentation, etc.
Quick links
Installation
KerasCV requires Python 3.7+ and TensorFlow 2.9+.
Install the latest release:
pip install keras-cv --upgrade
You can also check out other versions in our GitHub repository.
Quick Introduction
Create a preprocessing pipeline:
import keras_cv
from tensorflow import keras
preprocessing_model = keras.Sequential([
keras_cv.layers.RandAugment(value_range=(0, 255))
keras_cv.layers.CutMix(),
keras_cv.layers.MixUp()
], name="preprocessing_model")
Augment a tf.data.Dataset:
dataset = dataset.map(lambda images, labels: {"images": images, "labels": labels})
dataset = dataset.map(preprocessing_model)
dataset = dataset.map(lambda inputs: (inputs["images"], inputs["labels"]))
Create a model:
densenet = keras_cv.models.DenseNet121(
include_rescaling=True,
include_top=True,
num_classes=102
)
densenet.compile(optimizer='adam', metrics=['accuracy'])
Train your model:
densenet.fit(dataset)
Citing KerasCV
If KerasCV helps your research, we appreciate your citations. Here is the BibTeX entry:
@misc{wood2022kerascv,
title={KerasCV},
author={Wood, Luke and Zhu, Scott and Chollet, Fran\c{c}ois and others},
year={2022},
howpublished={\url{https://github.com/keras-team/keras-cv}},
}