KerasCV is a repository of modular building blocks (layers, metrics, losses, data-augmentation) that applied 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.

KerasCV can be understood as a horizontal extension of the Keras API: the components are new first-party Keras objects (layers, metrics, etc) that are too specialized to be added to core Keras, but that receive the same level of polish and backwards compatibility guarantees as the rest of the Keras API and that are maintained by the Keras team itself (unlike TFAddons).

KerasCV's primary goal is to provide a coherent, elegant, and pleasant API to train state of the art computer vision models. Users should be able to train state of the art models using only Keras, KerasCV, and TensorFlow core (i.e. tf.data) components.

To learn more about the future project direction, please check the roadmap.

• Contributing Guide
• Call for Contributions
• Roadmap
• API Design Guidelines

If you'd like to contribute, please see our contributing guide.

To find an issue to tackle, please check our call for contributions.

Thank you to all of our wonderful contributors!

Many models in KerasCV come with pre-trained weights. With the exception of StableDiffusion, all of these weights are trained using Keras and KerasCV components and training scripts in this repository. Models may not be trained with the same parameters or preprocessing pipeline described in their original papers. Performance metrics for pre-trained weights can be found in the training history for each task. For example, see ImageNet classification training history for backbone models here. All results are reproducible using the training scripts in this repository. Pre-trained weights operate on images that have been rescaled using a simple 1/255 rescaling layer.

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}},
}