KerasCV and KerasNLP: Vision and Language Power-Ups
<|reference_start|>KerasCV and KerasNLP: Vision and Language Power-Ups: We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at the library's lowest level of abstraction, we provide building blocks for creating models and data preprocessing pipelines, and at the library's highest level of abstraction, we provide pretrained ``task" models for popular architectures such as Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. Task models have built-in preprocessing, pretrained weights, and can be fine-tuned on raw inputs. To enable efficient training, we support XLA compilation for all models, and run all preprocessing via a compiled graph of TensorFlow operations using the tf.data API. The libraries are fully open-source (Apache 2.0 license) and available on GitHub.<|reference_end|>
@article{watson2024kerascv,
    title={KerasCV and KerasNLP: Vision and Language Power-Ups},
    author={Matthew Watson, Divyashree Shivakumar Sreepathihalli, Francois
  Chollet, Martin Gorner, Kiranbir Sodhia, Ramesh Sampath, Tirth Patel, Haifeng
  Jin, Neel Kovelamudi, Gabriel Rasskin, Samaneh Saadat, Luke Wood, Chen Qian,
  Jonathan Bischof, Ian Stenbit, Abheesht Sharma, Anshuman Mishra},
    journal={arXiv preprint arXiv:2405.20247},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20247},
    primaryClass={cs.AI cs.CV cs.LG cs.SE}
}